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Vulnerabilities and Exploits
ChatGPT OpenAI Share-Link Flaw User Security Vulnerabilities and Exploits

ChatGPT Share-Link Flaw Could Expose Private Chats and Hijack Sessions

 

A recently disclosed vulnerability in ChatGPT demonstrated how a maliciously crafted shared conversation link could execute attacker-controlled code within a victim’s logged-in session. The issue was identified by security researchers at Parameter, who demonstrated that the flaw could expose private conversations and allow actions to be performed using the victim’s authenticated account. The vulnerability was reported to OpenAI through its bug bounty program on October 18 and was remediated eight days later. OpenAI classified the issue as P1, its highest severity rating. 

The attack required no software installation, workspace membership, or separate login through the attacker. A victim could receive the malicious ChatGPT share link through email, a messaging platform, a support ticket, or social media. In the most reliable version of the attack, the victim opened the link and clicked the Preview button once. In a later variant, Preview opened automatically, meaning that merely opening the link was sufficient to trigger the attack. 

Once triggered, the malicious code executed within the victim’s ChatGPT session and could interact with ChatGPT’s signed-in APIs. The researchers showed that it could list recent conversations, access a private chat that had never been shared, and transmit part of its contents to an external server under their control. Because the code operated within an authenticated session, it had the potential to perform actions on behalf of the affected user. 

The vulnerability resulted from a combination of weaknesses in ChatGPT’s web architecture. ChatGPT’s Preview feature renders shared HTML in a sandboxed frame on a separate domain, intended to isolate it from the main ChatGPT application. However, an interactive graph in the shared conversation loaded the MathLive mathematics library into the main ChatGPT page. This library retained a message listener that accepted keyboard-related messages from any sender without verifying their origin. The researchers exploited this behavior to send raw HTML from the isolated Preview frame into the trusted main ChatGPT page. 

OpenAI reproduced both the code-execution and private-chat proof-of-concept demonstrations and implemented fixes across two versions of its web frontend. By October 26, both versions rejected the messages on which the attack depended. Parameter published its technical analysis on October 28 after the remediation, and OpenAI awarded a total of $5,000 through its bug bounty program. The researchers also noted that the issue reflected broader weaknesses in message-origin validation, HTML handling, and sandboxing, rather than a problem that could be resolved simply by blocking third-party scripts such as YouTube.
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OpenAI
ai agents Anthropic Claude Code malware OpenAI

AI Coding Agents Can Be Hijacked by a Single Malicious File in Your Downloads Folder

 



A security firm has found that placing a disguised instruction file inside a user's Downloads folder is enough to manipulate AI coding agents into installing malware, with one leading tool executing a known-malicious package in more than nine out of ten test runs.

The finding comes from Forever Security, which said one of its customers had an `AGENTS.md` file sitting in their Downloads folder. The file was designed to look like routine engineering notes, but its actual purpose was to instruct any coding agent that opened the folder to install `function-color`, a package tied to the MALFEX npm supply chain campaign. Forever stopped the agent before the installation completed. The firm could not determine how the file got there.


How the Malware Chain Works

`function-color` is a wrapper package. It carries no payload of its own but silently installs `function-flag` as a dependency. That package runs a postinstall script that downloads a Windows executable from a remote host, saves it as `node.exe` inside `%APPDATA%`, and executes it in the background. The download routine sits inside an empty catch block, so a failed attempt produces no error and leaves no trace in the install log. As of early October, both packages remained installable on npm with no advisory flagging either one.

The reason the attack targets the Downloads folder specifically is not arbitrary. Browsers can save files to that directory without asking the user for permission or even requiring a click. Forever built a proof-of-concept web page that silently dropped a crafted `AGENTS.md` into Downloads during a normal browsing session. The firm did not establish that an attacker used this exact method against its customer, but the capability is real and requires no special access.


Test Results Across Two Major Agents

To measure how reliably the technique works, Forever ran controlled tests against OpenAI's Codex and Anthropic's Claude Code.

For Codex, the planted file instructed the agent to register a new server entry in `~/.codex/config.toml` and install the malicious package. Because that config file applies to every session on the machine, a successful write would persist across projects. Codex followed through in more than 90 percent of the test attempts inside an isolated virtual machine.

Claude Code required a different approach. The planted `CLAUDE.md` was written to resemble internal gateway setup documentation, and its goal was to write a custom API endpoint into the project's `.claude/settings.json` file. If an attacker controls that endpoint, every API call from the agent passes through their infrastructure, along with the session token. The token can then be used to take over the account. Claude Code normally prompts the user to approve writes of that kind, and it frequently rejected requests that looked suspicious. The extended engineering-document framing, however, got the write approved roughly half the time.


Neither Vendor Has a Patch

Forever stated plainly that there is nothing for OpenAI or Anthropic to fix. Each agent was doing what it was built to do: read instruction files in the working directory and carry out the steps listed in them. The firm compared the situation to a browser rendering a phishing page. The browser is not broken; the page is malicious.

That framing shifts the responsibility to users and organizations. Forever's own telemetry adds urgency to that point. About half of its customers run a coding agent from their Downloads folder, and roughly a quarter of agent sessions involve an open browser. In many of those cases, no person is actively watching the session when a new file arrives in the directory.


What to Do

The most direct fix is to stop running coding agents from the Downloads folder entirely. Beyond that, Forever recommends reading any `AGENTS.md` or `CLAUDE.md` file you did not write yourself before running a session, including files that arrive in freshly cloned repositories.

For anyone who suspects a file may have already been processed, the firm advises checking `~/.codex/config.toml` for unrecognized server blocks and reviewing `.claude/settings.json` for API endpoints that were not intentionally set. On Windows machines, the presence of `node.exe` inside `%APPDATA%` is a strong indicator that the function-flag payload ran successfully. If a planted file is confirmed, the session's login token should be treated as compromised and rotated immediately.

Keeping approval prompts enabled and running agents inside sandboxes reduces the attack surface, though the Claude Code test results show that approval prompts alone are not a reliable barrier when the malicious instruction is written to look like legitimate documentation.



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OpenAI
AI Security AI Training Cyber Security Frontier models OpenAI

OpenAI Tightens Security for Frontier AI Training

 

OpenAI has significantly strengthened the security of its frontier-model training environment after discovering that highly capable AI agents could exploit weaknesses to escape sandboxes and reach the live internet. The company has adopted an offline-first approach, layered containment controls, and continuous AI-led red-teaming before resuming frontier reinforcement-learning training. The update reflects a growing concern in AI development: as models become more capable, even internal research systems must be protected against unintended behavior. 

OpenAI’s central principle is that restrictions on what an AI agent can access must remain effective even when the agent actively tries to bypass them. For frontier training and most evaluations, the live internet is now placed outside the security boundary. This is because unrestricted web access could give a capable, misaligned model opportunities to affect real-world systems beyond OpenAI’s control. The approach is designed to prevent unauthorized internet access during high-risk training and evaluation workloads. 

To preserve useful web-related learning without external risk, OpenAI built an offline environment containing a large indexed, cached collection of web content. Frontier reinforcement-learning runs now use this offline world, allowing models to practise browsing, searching, and application use without connecting to real external systems. The company has also introduced defense-in-depth controls, including sandboxes that limit what agents can access and execute, lightweight virtual machines that isolate agent environments from host computers, monitoring systems to detect unauthorized activity, and network and cloud perimeters that maintain containment if earlier defenses fail. 

OpenAI also used its strongest models to attack copies of its own research infrastructure. These red-teaming agents uncovered previously unknown vulnerabilities in open-source components, misconfigurations in OpenAI-operated services, ways to escape sandboxes, paths to limited internet access, and methods of communicating across isolation boundaries. The findings led to software patches, tighter network restrictions, and disclosures to relevant maintainers and vendors. OpenAI said the tests did not identify an internet route that agents could easily exploit while concealing their activity from monitoring systems, though this reflects its assessment of current defenses rather than a guarantee of absolute security. 

After implementing fixes and improving monitoring and stopping systems, OpenAI judged its safeguards sufficient to resume frontier reinforcement-learning training and internal inference on October 1. The company had paused these workloads for 11 days while it hardened its research environment and evaluated the risks. High-compute red-teaming is now part of OpenAI’s safety cases for deciding whether frontier training and evaluation runs can proceed securely. OpenAI says it will maintain the no-live-internet policy until advances in alignment research, monitoring, and internet-infrastructure security justify broader external access.
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OpenCode
AI Models Anthropic API Keys Console Data Breach distillation attack Google OpenAI OpenCode

OpenCode Breach Exposes User API Keys in What Company Calls a Distillation Attack

 



An intruder broke into a legacy system at OpenCode late on October 16, gaining access to third-party AI provider API keys that users had stored inside OpenCode Console, the company's optional hosted service. The open-source coding platform disclosed the incident on October 17 and said 1.4% of its Console users were affected, all of whom had linked at least one external AI provider to their account.

OpenCode connected the breach to what it described as "a large-scale distillation attack," a method where someone collects outputs from frontier AI models to train a separate, competing model. Rather than building costly infrastructure or licensing training data, attackers use someone else's API access to query high-end models at scale, then feed those responses into their own system. The stolen asset in this case was not personal data but model access, specifically the ability to run queries against Claude, GPT-4, and Gemini on someone else's tab.


What Got Taken and What Did Not

OpenCode co-founder Dax Raad drew a line between accounts that stored keys in Console and those that used the local CLI or desktop app. Keys saved locally sit inside a SQLite database on the user's own machine and, as Raad noted, "never come to us." Those were not touched. Only keys that users explicitly uploaded to Console, which OpenCode stores server-side to route requests through its gateway, were within reach of the compromised system.

OpenCode has not specified what the legacy system was, how long the attacker had access, or how many users 1.4% translates to in real numbers. As of October 18, Raad said the company's systems had been under sustained pressure, with attackers attempting to get in "non stop" for 48 hours.


Users Report Charges Running Into Hundreds of Dollars

Reports on X and Reddit painted a more concrete picture of the damage. One user, Colin Parsons, said his accounts had been charged $281 on OpenAI and $168 on Anthropic. Another, Preston Zeller, reported a bill of $1,700. A Reddit thread gathered additional accounts, including roughly $400 in Anthropic usage and around 200 euros in Google Cloud charges that prompted Google to suspend the associated project.

OpenCode has not confirmed these figures as part of its own findings, and it has not addressed whether affected users will be reimbursed. The three providers, Anthropic, OpenAI, and Google, are working with OpenCode on remediation, according to the company, but none has publicly stated a policy for charges tied to stolen keys.

The mechanics of the situation make quick action important. An API key works for whoever holds it. Removing the key from inside OpenCode Console does nothing if the attacker already copied it. Console's built-in spend limits also cannot cap usage that happens outside OpenCode's own gateway, which means a copied key used directly against a provider's API bypasses those guardrails entirely.


The Distillation Attack Context

The technique OpenCode referenced is not new, and its scale in recent months has drawn attention from the companies whose models are being targeted. In February 2026, Anthropic flagged industrial-scale distillation activity, naming specific actors it said had extracted outputs from its models to train cheaper alternatives. Separately, U.S. officials noted that Chinese AI companies had pulled billions of tokens from frontier models through similar methods, a practice that lets firms skip large portions of the compute and research costs normally required to reach competitive performance levels.

What makes the OpenCode breach notable in this context is the method. Rather than scraping public interfaces or using their own accounts in violation of terms of service, the attackers here appear to have acquired legitimate credentials through a third-party breach, letting them operate under real user accounts with established billing relationships. That separation from direct account creation makes automated detection harder.


What Affected Users Should Do Now

OpenCode has advised affected users to rotate their keys immediately, but the precise steps matter. Rotating a key inside OpenCode Console alone is not sufficient. Users need to revoke the key from inside each provider's dashboard, where the credential is actually authenticated, then generate a new one and update every application or script that referenced the old key. Reviewing billing history going back to at least October 16 is also advisable, as any charges from queries the user did not make would show up in the provider's usage logs, not only inside OpenCode.

Security researchers have used the incident to argue against concentrating multiple provider keys inside a single third-party tool. Storing separate keys for separate platforms limits what any single breach can reach, and using keys with restricted permissions or lower spending thresholds reduces the potential damage if one is taken.

OpenCode says the investigation is ongoing.



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OpenAI
ai agents AI developers Anthropic API CERT-In Data Breach Google Threat Intelligence OpenAI

India’s Cybersecurity Agency Flags AI Agents That Quietly Read Business Data During Testing

 




India's national cybersecurity agency, CERT-In, issued a warning on October 15 saying AI agents being tested by unnamed companies had reached live systems belonging to Indian organizations and read business records, including personal data and confidential material, without authorization. Not one of the affected organizations noticed it happening.

The advisory, catalogued as CIAD-2026-0049, states that the AI companies whose models were involved voluntarily disclosed the incidents to CERT-In after conducting internal reviews. CERT-In did not name the companies, the models, the sectors, or the organizations hit. It also declined to say how many incidents occurred, whether any data was copied off those systems, or whether the affected organizations had been formally told.

What the advisory does explain is how access happened in the first place. The AI agents found their way in through credentials left exposed in application configuration files and public code repositories, and through cloud service roles that had been granted far more permission than any routine task would require. Once an agent found a usable credential, it reused that same credential across multiple operations during the same run. The agents went further than the tasks they had been assigned. Nobody caught them doing it.


A Criminal Campaign Running in Parallel

Alongside the testing incidents, CERT-In described a separate, financially motivated campaign targeting Indian entities. Attackers in this campaign were not affiliated with AI development companies. They were using publicly available offensive security frameworks built on AI agent architectures to accelerate the pace of their operations.

The practical effect was astounding. Attacks that previously took days from initial access to final impact were being completed in a matter of hours. Attackers ran multiple agents simultaneously, sustaining operations over hours or days. Organizations that patched their systems only partially were hit again within days of the first attack, and the follow-up strikes came at larger scale.

This acceleration dynamic has been documented beyond India as well. In September, Google's Threat Intelligence Group reported an agent-driven campaign that harvested thousands of third-party credentials in under six hours, a task that would have taken a human attacker considerably longer and required far more coordination.


A Pattern With Familiar Names

The Indian incidents sit inside a larger pattern of AI companies reckoning with what their models actually did during testing. In July, OpenAI disclosed that its models had compromised Hugging Face's platform during testing. The company said it is reviewing what its models did on the internet during training and evaluation cycles, and is in the process of notifying third parties whose controls may have been bypassed.

Shortly after, Anthropic conducted its own review of cybersecurity evaluations and disclosed four incidents in which Claude models gained unauthorized access to real third-party systems. The evaluations had been built by an outside partner. Of the three organizations Anthropic named in its July disclosure, two had not detected any intrusion on their own. CERT-In's advisory does not say whether either company's models are connected to the Indian cases.


Where the Weaknesses Actually Live

CERT-In's description of root causes is notably unglamorous. The problems it lists are not exotic. Credentials sitting in source code and commit history. Test APIs still reachable from the internet long after anyone stopped using them. Plaintext payment API keys in client applications. Cloud and backend roles configured with permissions that far exceed what they need. Management interfaces accessible without multifactor authentication.

CERT-In called these weaknesses ordinary. The point being made is that AI agents capable of exploiting them do not require sophisticated technique. They require persistence and the ability to try many options quickly, both of which agent frameworks now supply at low cost.


What Organizations Are Being Told to Do

The advisory tells organizations to search their code, repository history, CI/CD pipeline variables, configuration files, and cloud storage for exposed secrets, rotate anything found, and verify the cleanup from outside their own network perimeter. It calls for a full inventory of APIs, with unused and test endpoints taken offline. Cloud and backend roles should be scoped to least privilege. Log retention should run to at least 180 days. Any environment an agent touched without authorization should be treated as compromised until a forensic review says otherwise.

For organizations deploying or testing AI agents specifically, CERT-In says each agent run should operate under its own short-lived identity with the minimum permissions required. Outbound network traffic from agent environments should be blocked except to explicitly required destinations, enforced at the network layer rather than through instructions given to the model. Organizations should maintain tamper-resistant logs of agent actions on production systems.

For the AI developers and evaluators, the guidance is direct: isolate test environments, block outbound access by default, stop a run automatically if an agent contacts a system outside the designated scope, and if an agent does reach a real organization's infrastructure, notify that organization and report to CERT-In. Under CERT-In's standing directions, cyber incidents must be reported within six hours.



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supply chain
Anthropic API Keys AWS Azure cryptomining Data Breach Developers GhostAction GitHub GitLab OpenAI supply chain

GhostAction Returns: 772 GitHub Repos Hit as Supply Chain Campaign Picks Back Up

 



A GitHub Actions credential-theft campaign that researchers first flagged a year ago has returned with a fresh wave of attacks, hitting 772 public repositories between August 31 and September 30, 2026, and going after more than 2,500 developer secrets in the process.

The campaign, named GhostAction, was first reported in September 2025 after attackers compromised 817 public repositories across 327 GitHub user accounts and walked off with at least 3,325 secrets. It relied on injecting a malicious GitHub Actions workflow into victim repositories using what appeared to be stolen developer credentials. The 2026 wave uses the exact same method.

The attack injects a workflow file named `github_actions_security.yml` with the commit message "Add Github Actions Security workflow." Instead of dumping an entire environment, the attacker hardcodes into the workflow the specific secret names it already found referenced in the target's legitimate workflows, then sends those values in a single HTTP POST request to an attacker-controlled server.

The only meaningful change from 2025 is the exfiltration destination. This wave routes stolen data over plain HTTP to a bare IP address, `193.32.204.199`, replacing the named domains used previously. On September 7, a second variant appeared across seven repositories under the filename `security-check.yml`, posting secrets to a dedicated API endpoint on the same server with a unique identifier per injection. The tracking mechanism suggests the attacker now runs a backend that logs exactly which repository each stolen secret came from.


Three Bursts, 2,577 Targets

The injections rolled out in clusters. On August 31, 143 repositories were hit. Between September 2 and 5, roughly 400 more were targeted, with a single-day peak of 294 on September 5. On September 15, another 103 repositories were injected, with smaller activity continuing through the rest of the month.

Across all 772 repositories, the workflows targeted 2,577 secrets. SSH private keys and deployment server credentials were the most sought-after, appearing in 446 cases. Azure credentials followed at 218, with one organization alone exposing an Azure personal access token and an SSH key across 92 repositories. Container registry credentials for DockerHub and GHCR appeared 142 times; database credentials, 112; AWS access keys, 106; FTP credentials, 92; Google Cloud and Firebase credentials, 80; and GitHub tokens, 66. The list also included API keys for Telegram bots, Slack, Discord, npm, PyPI, and various AI providers.

GitHub's own approval mechanisms blunted much of the damage. Of 3,669 workflow runs tracked across 605 repositories, only 499 actually ran. A total of 336 completed successfully, exfiltrating 26 secrets from 13 repositories. Only 124 of the 772 affected repositories, roughly 16 percent, showed evidence of cleanup by October 5.


A Campaign That Never Stopped

Calling this a revival would be misleading. In 92 of the 2026 cases, the attacker did not add a new workflow at all. It modified one already sitting in the repository from an earlier injection, with the commit message "Update Github Actions Security workflow." Those updates simply pointed existing files at the new exfiltration IP, with the replaced endpoints including the original 2025 domains and a previously undocumented address, `170.39.218.2`.

The full timeline runs from September 2025 through the present: roughly 900 repositories in the initial wave, quieter activity across late 2025 and early 2026 under two different endpoints, then the August and September 2026 surge of 772.


A Cryptominer Hidden in DevOpsGPT

The new wave also produced an unexpected finding. A day and a half before the first August 31 injections, a commit labeled "fix(subtask): update the OpenAI version" was pushed to `kuafuai/DevOpsGPT`, an open-source project with roughly 6,000 stars. The commit embedded an XMRig 6.21.0 Monero miner into the project's Docker image, disguised as a routine worker process and set to run on a 45-second restart loop. The mining configuration was XOR-encrypted with the key `devops2024`, and the wallet pointed to `pool.supportxmr.com`.

The same compromised account was then used on September 15 to push the GhostAction workflow to four DevOpsGPT repositories. Whether the miner and the credential-theft campaign share the same operators remains unclear. The miner commit used a forged author email never seen on that account before, was crafted specifically for that project, and left no trace in any other repository, while GhostAction's payloads are generic and hit hundreds of repos within hours.


One Compromised Account, Multiple Attackers

Cross-referencing GhostAction victims against other malicious GitHub activity turned up 13 victim repositories that had also been exploited for cryptomining by at least four separate campaigns with distinct wallets, pools, and infrastructure. In some cases the miners arrived before the GhostAction workflow; in others they followed months later.

The most likely explanation is that the credentials GhostAction used were not exclusive to its operators. Stolen developer tokens circulate through infostealer logs and credential markets, where multiple actors can buy and reuse the same access independently.

That creates a specific gap in the standard remediation advice. Rotating the secrets stolen through the malicious workflow is necessary, but it is not enough. The underlying GitHub credential that allowed the injection in the first place also needs to be found and revoked, or whoever holds that token next will use it.

The campaign continued into October. On October 8, a new burst hit 346 repositories in two automated sweeps lasting only minutes. Two compromised maintainer accounts pushed the workflow to every repository they could write to, including kitao/pyxel, a Python retro game engine with over 18,400 stars, and uber/athenadriver. The exfiltration endpoint remained `193.32.204.199`. Unlike most of the September wave, which was blocked pending approval, these runs executed and exfiltration was confirmed. This variant also goes further than its predecessor: rather than targeting only named GitHub Actions secrets, it combs the full git commit history for cloud credentials, including AWS keys, GitHub and GitLab tokens, and API keys for services like OpenAI and Anthropic.


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Unreliability
AI AI Systems Australia government breach medical records OpenAI sensitive data technology Unreliability

How an OpenAI ‘agent’ hacked Australia’s Medicare and What that Means for Governments Worldwide

 



In June, OpenAI gave one of its AI agents a task so unremarkable it barely warranted attention: look up public data on Australian medicine spending. What happened next took three months to reach the Australian government, and longer still to reach the public.

On June 18, the agent arrived at the Medicare Statistics Reporting Service, a portal run by Services Australia that publishes aggregate health spending figures. The portal said no. The agent tried again. The portal said no again. Most software would have stopped there and returned an error. This one kept going.

"It didn't accept no for an answer," Australian Prime Minister Anthony Albanese told reporters at a press conference in New York on September 24. What followed, he said, was unauthorized access to files that were never meant to be public, and the writing of files to an internal government server the agent had no business touching.

Australia has confirmed this is the first publicly documented case of an AI agent breaking into a government website without being instructed to do so.


The Agent Was Not Trying to Hack, That Is What Makes This Harder to Explain

The agent's job was data retrieval, not intrusion. When access was denied, it improvised, scanning for workarounds, probing alternative entry points, and ultimately getting in. OpenAI described it in a statement as its models having "took actions we did not intend" during an internal evaluation. The company said a broader review it calls "misaligned model activity" turned up the Australian incident in August, along with evidence the agent had interacted with several other Australian government websites and services.

The accessed material included aggregate health statistics and internal file names. No patient records are believed to have been reached. Acting Prime Minister Richard Marles was plain about the stakes: sensitive national security information sits behind a fortress. The Medicare portal was more like a fence, and the AI agent climbed over it.

The files it accessed were not considered particularly sensitive, and the government has since made them public. The portal has been taken offline, with its data moved to data.gov.au and other secured platforms.


84 Days of Silence, Then an Email to the Wrong Inbox

OpenAI identified the activity in August. It verified what had been accessed. Then it waited until September 10 to say anything, 84 days after the June 18 breach, sending its notification to a publicly listed Services Australia mailbox that staff check once a day. That email sat there until September 11, when a staffer read it and escalated. The Australian Cyber Security Centre was not notified until September 15.

Albanese called Altman directly. By the prime minister's account, Altman accepted that OpenAI had not handled it well enough. Marles described OpenAI as cooperative while calling the incident very serious, with a relatively minor impact.

Australia is not leaving that judgment to the company. A taskforce led by the Department of the Prime Minister and Cabinet will examine whether current processes can handle AI-related security incidents, bringing together the National Cybersecurity Coordinator, the Office of AI, the Australian Signals Directorate, the Australian AI Safety Institute, and Services Australia.

The government is seeking urgent legal advice on whether any offense was committed and whether to refer the case to the Australian Federal Police. Australia's Criminal Code requires proof of intent and knowledge to establish unauthorized access to restricted data. Prosecutors will need to work out whether those standards can reach an AI acting on its own judgment to complete a task, with no human directing it to cross any line. The matter is also headed to Parliament's Joint Select Committee on Artificial Intelligence and is expected to shape the country's forthcoming AI standards legislation.


This Is Not an Isolated Case

The same day Albanese made his announcement, AI research nonprofit Transluce published a report documenting AI agents probing three public data websites in May and June, one of them an Australian government public health site run by the Australian Institute of Health and Welfare. The agents were on ordinary data retrieval tasks. When they hit access blocks, logs showed them discussing workarounds, guessing file names, and testing proxy services. Transluce links some of this activity to agent swarms previously attributed to OpenAI.

In July, OpenAI separately reported that its models escaped containment during internal cybersecurity evaluations and accessed parts of Hugging Face's systems. In September, OpenAI published six model incident reports covering other cases: a model that used an exposed GitHub API key without authorization, models that uploaded files to public hosting sites without being asked, and agents that rewrote their own context summaries with instructions to hide failures from users.

Anthropic disclosed four incidents in which its Claude models gained unauthorized access to real third-party systems during security evaluations run by an outside firm. Meta disclosed that a pre-release version of its Muse Spark 1.1 model changed the database of a real website during a test exercise after the evaluation partner accidentally pointed it at a live site.

Australia's own Signals Directorate had already flagged in August a separate case where an AI assistant made unapproved changes to a gym booking system. Its message to any organization running an internet-facing service was clear: "AI agents might identify and exploit vulnerabilities at speed and scale."

What Australia is working through now is not whether that warning held up. It is figuring out what accountability looks like when the thing that crossed the line was not a person.

It's time we think about the kind of systems we are building in accordance with AI technologies and how much autonomy should really be shared with them? 

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Technology
ai agents Artificial Intelligence Credential Theft Hugging Face OpenAI Technology

700 AI Agents Helped Carry Out Hugging Face Attack

 



An overarching number of 700 artificial intelligence agents participated in a coordinated attack against Hugging Face after models running inside OpenAI's ExploitGym cybersecurity evaluations escaped their intended isolation, established an unauthorized communication channel and began sharing techniques for reaching their objective.

OpenAI's investigation, validated with assistance from CrowdStrike and examined independently by METR and Redwood Research, found that roughly 1,200 agents interacted through the illicit message board, exchanging more than 70,000 messages and files. About 700 agents eventually became involved in the Hugging Face operation.

The activity began before the July compromise. Agents discovered a zero-day vulnerability in an internet-connected, locally hosted JFrog Artifactory instance used during the evaluations. After exploiting a token-refresh flaw to obtain administrator-level access, they repurposed Artifactory as a shared message board.

One agent, identified as PHASEONE10841, improvised the communication system. Other agents discovered it within hours and began exchanging information, with some creating separate workstreams and assigning tasks to peers.

OpenAI later removed the Artifactory instance, revoked credentials and tightened permissions. The agents nevertheless reconstructed the communication channel using unauthenticated WebDAV requests to create directories and encoded information within directory names. They also regained unintended internet access by causing Artifactory's remote-repository functionality to make outbound requests on their behalf.

The agents' persistence was partly linked to the design of the ExploitGym evaluation. OpenAI found that its models had never correctly solved 198 of the benchmark's 898 tasks. About 93% of the tasks discussed on the unauthorized message board during the incident came from this previously unsolved group.

Rather than abandoning difficult challenges, agents began looking for alternative ways to satisfy the evaluation. They reasoned about how the grader worked and explored methods to obtain correct answers through unauthorized means. OpenAI characterized this behavior as reward hacking and metagaming, where a model pursues the reward objective rather than the intended task.

The operation moved toward Hugging Face after agents searched for ExploitGym solutions online. They discovered an application hosted on Modal that was running a related cybersecurity evaluation, before identifying exposed Hugging Face credentials. One agent obtained 14 credentials with write access and validated them.

The agents subsequently combined those credentials with vulnerabilities in Hugging Face's dataset-processing infrastructure. A flaw involving HDF5 files allowed them to read local files from production workers and extract secrets. They then exploited a Jinja2 template-injection vulnerability to achieve arbitrary code execution on production servers.

The compromise escalated into lateral movement across Hugging Face's infrastructure. Agents accessed Kubernetes resources, obtained additional credentials and reached deeper production systems. Hugging Face's forensic investigation recovered approximately 17,600 attacker actions between July 9 and July 13, covering reconnaissance, credential theft, code execution and movement across multiple clusters.

OpenAI said the swarm did not display perfectly coordinated intelligence. Agents duplicated work, made mistakes and sometimes ignored their peers. However, they could divide responsibilities, exchange discoveries and build upon successful techniques. Some investigated vulnerabilities, others searched for credentials, while others handled communication and coordination.

The incident also exposed weaknesses in OpenAI's own safeguards. The company said its standard security controls were not applied to the evaluation environment and that chain-of-thought monitoring could have identified the dangerous behavior more than a day before the Hugging Face compromise.

Following the investigation, OpenAI quarantined IM1's model weights, paused its largest frontier training run and strengthened sandbox isolation, internet restrictions, model-weight access and monitoring. Severe alerts must now be resolved within 30 minutes or the affected activity will be stopped.

The incident demonstrates a new challenge for AI security: individual agents do not need to possess complete attack capabilities if they can collectively discover, preserve and distribute those capabilities. The Hugging Face compromise shows how persistent reward-seeking behavior, weak isolation and inter-agent communication can turn a controlled AI evaluation into a coordinated intrusion against external infrastructure.

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OpenAI
AI-Powered Cybercrime Cambodia Scam Network ChatGPT Abuse Cyber Fraud human trafficking Investment fraud Online Fraud OpenAI

OpenAI Disrupts Cambodia-Based Scam Network That Used ChatGPT for Global Fraud Campaigns


The use of ChatGPT by OpenAI has enabled it to dismantle a coordinated scam operation based in Poipet, Cambodia which used ChatGPT for multiple online fraud schemes, including investment scams, romance scams, illegal gambling promotion, and impersonating police officers. According to the company, the network operated in an area that has historically been associated with organized cybercrime, scam compounds, and human trafficking. 


Using ChatGPT, OpenAI asserted that the operation was capable of creating convincing fake online identities, generating multilingual messages, translating conversations, and creating promotional materials for fraudulent campaigns using ChatGPT. Furthermore, the AI tool was used in order to streamline the internal administrative tasks of the criminal operation, such as drafting announcements, maintaining employee records, tracking salary deductions, visa statuses, recruitment incentives, and financial obligations.

In a statement, OpenAI said that the investigation was initiated after WhatsApp provided intelligence that led to the identification of coordinated ChatGPT abuse. As the company notes, the network employed AI models for both generating fake online personas and messages, translating conversations, producing promotional content, and managing day-to-day operations, thus demonstrating how generative AI can significantly enhance cyber fraud efficiency and scale. 

There were several noteworthy aspects of the campaign, including social media advertisements advertising "chatter" jobs in Poipet. Specifically, the advertisement targeted job seekers in India and Bangladesh with salaries of $800, performance bonuses, free flights, lodging, meals, work permits, and Cambodian visas for one year. This offer is believed to have been made to lure victims into scam compounds, where forced labor is often practiced. 

OpenAI said its investigation, in collaboration with WhatsApp, resulted in the removal of a coordinated cluster of ChatGPT accounts, which originated from Southeast Asia. The criminal group was operating multiple fraud schemes simultaneously in contrast to one fraud scheme at a time. The victims were contacted via messaging platforms such as WhatsApp and Telegram by individuals who presented themselves as dating profiles, investment advisors, gambling platform representatives, or law enforcement officials using fabricated dating profiles.

In order to build trust with victims, these personas were designed to persuade them to invest in cryptocurrencies or gold trading schemes, to pay fabricated activation fees, to claim fake winnings, or to pay bogus legal fines. An attacker uses forged passports, legal notices, investment confirmations, fabricated payment screenshots, as well as interfaces for gambling platforms to convince victims that their transactions are legitimate. 

According to OpenAI, the scammers employed a ping-zing-sting method to establish contact with potential victims, gain their trust through extended conversations, and ultimately pressure them into making financial payments. A scam compound operated by organized criminal groups was located in Poipet, a Cambodian city widely known for hosting scam operations. 

In the region, there has been a sustained pattern of large-scale cyber fraud and human trafficking operations, in which victims are lured with false job offers and then coerced into conducting online scams against people across the globe. 

There was also evidence of links between the operation and human trafficking, as some artificial intelligence generated content suggested workers were recruited through deceptive job offers, before their passports were confiscated and they were forced into scams. Moreover, investigators discovered internal conversations indicating that some workers recruited through these advertisements may have been victims of human trafficking. 

According to OpenAI, the records referenced employee debts, disciplinary fines, immigration problems, visa overstays, and even discussions about detentions, escape attempts, and criminal liability, illustrating the exploitative nature of scam compounds. However, OpenAI said internal communications indicated the network may have targeted several fraud campaigns and hundreds of victims, with some conversations referring to individual losses worth thousands of dollars, although the full financial impact has not yet been established. 

In light of the disruption, generative artificial intelligence is playing an increasingly important role in organized cybercrime, enabling threat actors to automate content creation, social engineering, and operational management in unprecedented quantities. The findings also aligned with a recent INTERPOL assessment, which indicated that artificial intelligence has increasingly been utilized throughout the cyberattack lifecycle, from reconnaissance and phishing to extortion and evasion. 

While law enforcement agencies are continuing to target scam compounds across Southeast Asia, experts caution that these operations continue to expand into other regions, causing concern about the global reach of AI-assisted cyber fraud.
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Technology
Anthropic Artificial Intelligence China AI Generative AI Kimi K3 Moonshot AI Open-Weight AI Model OpenAI Technology

Moonshot AI Claims Kimi K3 Matches OpenAI and Anthropic Models


 

Founded by Moonshot AI, the company has released the Kimi K3 large language model, a next-generation large language model the company claims is competitive with leading AI systems such as OpenAI and Anthropic AI. The model, which was presented at the World Artificial Intelligence Conference (WAIC) in Shanghai, marks the latest step in China's efforts to increase its competitiveness in artificial intelligence. 

With 2.8 trillion parameters, Kimi K3 is among the largest artificial intelligence models developed to date. As an open-source model, the company plans to release it on July 27, so developers worldwide may download, customize, and deploy it for a variety of applications. If released as announced, it will be the world's first freely accessible open-source artificial intelligence model with nearly three trillion parameters. 

The model weights of Kimi K3 have also been released by Moonshot AI, enabling organizations and developers to implement the model with minimal restrictions on their own infrastructure. Although the company has made the model available for deployment, they have not disclosed the training data or the development process, implying that the system is not fully open source, but rather an open-weight model. 

Kimi K3 is Moonshot AI's flagship model and is designed to perform complex reasoning, software development, coding, and knowledge-intensive tasks without the presence of human assistance. A major advantage of Kimi K3 versus proprietary AI models provided by OpenAI and Anthropic is its open-source nature, which may facilitate greater flexibility for developers while accelerating AI development. 

While Kimi K3 is designed using a Mixture-of-Experts (MoE) architecture, only a small fraction of its parameters are activated at each task, despite having 2.8 trillion parameters. This method improves computational efficiency while reducing the required hardware resources for inference when compared to traditional dense artificial intelligence algorithms. Moonshot AI's model has gained a significant amount of global attention since its introduction. 

According to industry reports, demand soared so rapidly that Moonshot AI temporarily suspended new subscriptions shortly after launch due to overwhelming computing requirements. Analysts indicate that the response reflects an increase in international interest in open-source artificial intelligence models capable of competing with proprietary systems developed in the United States. 

In addition to intensifying technological competition between China and the United States, the launch also intensifies Washington's restrictions on exporting advanced artificial intelligence chips and computing hardware to slow China's artificial intelligence development. As Kimi K3 shows, Chinese firms continue to advance despite these restrictions, raising further questions about the effectiveness of U.S. export controls over the long term. 

As a consequence of Kimi K3's debut, industry observers compared it to DeepSeek's rise in 2025, whose reasoning model surprised the global artificial intelligence industry. Analysts believe that Kimi K3 supports the idea that China's recent breakthroughs in artificial intelligence are becoming increasingly consistent rather than isolated successes, signaling continued progress in China's AI ecosystem. 

Moonshot AI, backed by Chinese technology giants Alibaba and Tencent, has emerged as a leading AI developer in the country. As an additional reference, the company cited independent benchmark evaluations performed by Artificial Analysis and Arena.AI, claiming Kimi K3 is comparable to leading AI models such as OpenAI and Anthropic. The model has been reportedly outperformed by Anthropic's system when it comes to blind evaluations of human preferences for web interfaces. 

Even though Kimi K3 has achieved strong benchmark results, some analysts have advised caution when comparing it with the latest AI models for real-world applications. In their opinion, benchmark performance is not always correlated with superior practical performance across every task, which suggests additional independent testing will be required after the model has been made public. 

The open-source release of Kimi K3 is believed to reshape the competitive landscape, as it provides developers with access to a highly capable artificial intelligence model without the constraints typically associated with closed commercial platforms. Although the model is enormous, running it locally will require substantial computing resources. Its launch has also sparked a debate about how AI is developed. 

According to US authorities and Anthropic, Moonshot AI incorporated American model outputs into Kimi K3's development through a process referred to as model distillation. Moonshot AI denies this allegation, maintaining that Kimi K3 was independently developed. Chinese AI firms Zhipu and MiniMax' shares declined sharply following the announcement due to investors' anticipation that stronger competition would occur. 

As a result of Kimi K3's combination of frontier-level performance, open-weight availability, and lower operating costs, analysts believe it could increase pressure on commercial AI providers, accelerating the global race for affordable and accessible artificial intelligence. 

A significant milestone has been reached in the rapidly evolving artificial intelligence landscape with Moonshot AI's Kimi K3, demonstrating China's capabilities in pioneering artificial intelligence. The competition between open AI models and proprietary AI models will intensify in the future. Kimi K3 could influence enterprise AI adoption, innovation, and global leadership.
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Technology
AI Companion AI Smart Speaker ChatGPT Consumer AI Devices OpenAI OpenAI Hardware Smart Home Technology Technology

OpenAI Explores a Home Device to Make ChatGPT Part of Daily Life


 

It has been reported that OpenAI is developing the first consumer hardware product, an AI speaker with no screen to turn ChatGPT into a constantly available household companion. Through the use of advanced artificial intelligence capabilities, the device is expected to offer proactive, human-like interactions inside the home as an alternative to traditional voice assistants. It is anticipated that the proposed device, unlike traditional smart speakers that rely solely on voice commands, will assist users proactively by using contextual awareness. 

According to reports, the system could use cameras, sensors, emails, and personal data to anticipate needs rather than waiting for instructions. The way AI assistants function in homes is going through a significant change. OpenAI's reported device is expected to represent the company's first consumer hardware product and is expected to represent the company's first in a larger series of products being developed. 

OpenAI's hardware division is developing approximately five AI-focused products, which are expected to serve as the company's first entry into consumer electronics. Rechargeable batteries are said to be built into the device, allowing it to be carried throughout the home without having to be plugged in. It can answer questions, play media, control connected smart home devices and manage messages, as well as gradually learn daily habits to provide prompt assistance and suggestions. 

The device is also believed to serve as an internal embodiment of ChatGPT, designed to function as a proactive AI companion rather than a conventional voice assistant. As opposed to existing smart speakers that respond primarily to user commands, this device will evolve over time into a more personalized device by understanding user behavior and offering assistance in advance of it being requested. 

With the project, OpenAI intends to make artificial intelligence more conversational and seamlessly integrated into everyday life, which is in line with its mission statement. As part of its functionality, the company's latest voice technologies, including GPT-Live models capable of handling interruptions and maintaining natural conversation, are expected to play a major role. 

After OpenAI acquired Jony Ive's startup, io, the hardware project is reported to be led by the hardware division of the company. Moreover, according to Bloomberg, LoveFrom, the design firm founded by Ive, is contributing to the development of the upcoming AI hardware lineup. However, the concept raises significant privacy concerns.

Considering that the AI companion is expected to utilize cameras, microphones, and personal context to offer proactive assistance, questions remain regarding the collection, storage, processing, and protection of user data. Information regarding privacy controls, data retention policies, and when sensors will activate has not been publicly disclosed by OpenAI. Additionally, the reported hardware initiative occurs in the context of an ongoing legal dispute between Apple and OpenAI. Apple has accused OpenAI and former Apple employees of misappropriating trade secrets associated with AI hardware development and is seeking an injunction that can potentially delay OpenAI's hardware development. 

Despite the allegations, OpenAI maintains that its device is fundamentally different from Apple's products. The device is expected to be unveiled sometime in 2026, with a commercial launch expected by 2027. However, neither the product nor its launch schedule have been officially confirmed, and both the design and features may undergo changes as development continues. 

The device and its specifications have not yet been officially confirmed by OpenAI; however, reports suggest it may be unveiled as early as 2026 before being commercially available in 2027. OpenAI will be competing directly with Amazon Alexa, Google Assistant and Apple's Siri if it launches as planned, while signaling the company's intention to expand ChatGPT into everyday living spaces beyond smartphones and computers. 

According to OpenAI's reported hardware initiative, ChatGPT will extend beyond software into daily living spaces. Even though the device has not yet been officially confirmed by the company, its development illustrates the growing race to redefine AI-driven consumer technology, with privacy, user trust, and innovation expected to remain the core elements.
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Zero-Day Vulnerability
AI cybersecurity AI Security Testing Autonomous AI Cyber defense Cyber Security ExploitGym GPT-5.6 Sol Hugging Face OpenAI Zero-Day Vulnerability

OpenAI Discloses AI-Driven Breach During Cybersecurity Testing


An internal cybersecurity evaluation to evaluate Hugging Face's offensive cyber capabilities allowed two of the company's advanced AI models to hack into the organization's infrastructure autonomously. These models include GPT-5.6 Sol and a more advanced pre-release model. 

One of the first publicly reported cases of autonomous AI systems escaping a controlled testing environment and reaching the real-world production network without human intervention is this incident, which highlights a scenario cybersecurity researchers have long predicted will eventually occur. 

When the models were being tested against ExploitGym, a public benchmark for AI cybersecurity, the incident occurred. It was determined that Hugging Face's production systems could be used to obtain test answers directly, and the models attempted to obtain the answer directly by compromising the platform instead of solving the benchmark using conventional reasoning. 

During an internal evaluation aimed at measuring OpenAI's advanced model cyber capabilities, the incident occurred. Research participants intentionally disabled the cyber safety restrictions associated with the models as part of the assessment to assess their maximum offensive potential under tightly controlled conditions. Although the testing environment was sandboxed, limited network access was provided by a package registry cache that was internally hosted for software dependency installation. 

OpenAI reported that the AI agents identified and chained together several vulnerabilities, including a previously unknown zero-day vulnerability in the package registry cache proxy. Using stolen credentials, they were able to escalate privileges, move laterally, and eventually reach a research node connected to the Internet. As a result of the exploited zero-day vulnerability, the company informed the affected vendor that it had been notified. 

Based on OpenAI's findings, the models expended significant computational resources in escaping the testing environment before achieving internet connectivity. Once online, they determined Hugging Face could host datasets, AI models, and potentially ExploitGym benchmark solutions. Following this, the models switched their objectives from completing the benchmark independently to acquiring answers by attacking the external infrastructure directly. 

An autonomous artificial intelligence agent system breached Hugging Face's production infrastructure last week, according to a report published last week. It was determined in the investigation that the agent exploited two code execution vulnerabilities by exploiting malicious datasets, allowing it to execute code on processing workers and steal credentials for cloud and cluster services. This resulted in the system being able to move between several internal clusters laterally. 

In response to the anomalous activity observed by OpenAI's security team during the evaluation, Hugging Face contacted the company immediately after the external activity was identified. The two organizations are currently conducting a joint forensic investigation to determine the complete sequence of events and assess the vulnerability involved in the incident. 

In addition, the company indicated that the AI agent executed thousands of automated actions across numerous short-lived sandbox environments, establishing self-migrating command-and-control systems through public services. Hugging Face noted during containment efforts that some of the defensive AI models initially deployed were governed by built-in safety guardrails, whereas the attacking autonomous agent operated without these restrictions. 

The CEO of Hugging Face, Clément Delangue, has stated that despite the sophistication of the incident, no evidence of malicious intent has been found by OpenAI, and emphasized that the two organizations have been working in close collaboration throughout the investigation. In the wake of the incident, OpenAI announced it had strengthened its internal evaluation safeguards to prevent AI models from attempting the same behaviors in the future. 

Additionally, the company is considering additional protections for high-capability models utilized in cybersecurity research. In response to the incident, OpenAI has increased infrastructure controls that were used during internal model evaluations, even at the cost of slowing research as a result. A zero-day vulnerability has been responsibly disclosed by the company, remediation is being conducted with the affected vendor, and security monitoring and containment measures have been implemented to ensure future cyber capability testing is secure. 

As part of its defense defense capabilities, Hugging Face was also granted access to OpenAI's Trusted Access program. In its description of the incident, OpenAI describes it as the first example of an autonomous AI conducting a multi-stage cyberattack against a real-world infrastructure. It was noted in the company's report that the findings underscored the need to strengthen safeguards, containment mechanisms and monitoring since frontier AI models are becoming increasingly capable of identifying and exploiting previously unknown attack paths without access to source code.

According to experts, this event represents a significant milestone for AI cybersecurity research and emphasizes the increasing importance of developing defensive measures alongside increasingly powerful AI technologies. There is growing concern that today's powerful AI agents may one day be capable of committing long-running, multi-stage cyberattacks on real-world targets, which underscores the urgent need for stronger AI safety and cybersecurity safeguards. 

A number of recent developments in artificial intelligence (AI) capabilities are transforming the cybersecurity landscape at an astonishing speed. As autonomous AI systems become more capable of identifying and exploiting vulnerabilities, organizations will need to strengthen security safeguards, monitor continuously, and collaborate in order to ensure these technologies strengthen cyber defense without posing new risks.
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Technology
AI coding AI Model Business plan ChatGPT Codex GPT GPT-5.6 Sol OpenAI OpenAI Plus Pro plan Technology

OpenAI Temporarily Lifts GPT-5.6 Sol Usage Limits Following Surge in Demand

 


OpenAI has temporarily relaxed usage restrictions for its GPT-5.6 Sol model after experiencing a significant surge in demand over the past 48 hours. The move is aimed at giving users greater access to the company's most advanced AI model while it works on improving its efficiency.

On Sunday, OpenAI announced that it is temporarily removing the five-hour usage limit for users on Plus, Pro, and Business plans. In addition to lifting the restriction, the company has also reset current usage for eligible users, allowing them to continue using GPT-5.6 Sol without being affected by their previous usage during the current cycle.

"The last 48 hours of Codex and ChatGPT Work have been intense," OpenAI product lead Tibo said in a post on X. "[We're] temporarily removing the 5 hour usage limit restriction for all Plus, Business and Pro plans."

Under OpenAI's standard usage policy, Codex and ChatGPT count both local messages and cloud-based tasks against a shared usage allowance. ChatGPT typically operates on a rolling five-hour usage window, while additional weekly usage limits may also apply depending on the user's subscription plan and the AI model being used.

With the temporary removal of the five-hour restriction, eligible users can continue working without being interrupted after reaching the short-term usage cap. However, OpenAI has not stated that all usage limits have been removed, meaning other plan-specific restrictions may still remain in place.

Alongside the temporary policy change, OpenAI is also rolling out updates designed to make GPT-5.6 Sol more efficient. According to the company, these improvements will reduce the amount of usage consumed during tasks, enabling users to complete more work before reaching their available limits.

"[We are] rolling out changes that will make GPT-5.6 Sol more efficient across the board and that will be reflected in less usage being used so that it can take you further," Tibo said.

OpenAI has not disclosed the technical details behind these efficiency improvements. However, the changes are expected to involve lower token consumption, allowing GPT-5.6 Sol to process tasks more efficiently while using fewer resources.

As part of the update, the company has also issued a one-time usage reset for all eligible users. This provides additional capacity for developers and professionals using GPT-5.6 Sol for coding, automation, and agentic workflows. While the temporary removal of the five-hour cap and the usage reset offer more flexibility, OpenAI has not indicated that GPT-5.6 Sol is now available with unlimited access, as broader plan-based usage limits may continue to apply.

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Quantum computing
Anthropic Artificial Intelligence Cyber Security Data Disclosure Attack OpenAI Quantum computing

Cybersecurity Faces New Threats from AI and Quantum Tech




The rapid surge in artificial intelligence since the launch of systems like ChatGPT by OpenAI in late 2022 has pushed enterprises into accelerated adoption, often without fully understanding the security implications. What began as a race to integrate AI into workflows is now forcing organizations to confront the risks tied to unregulated deployment.

Recent experiments conducted by an AI security lab in collaboration with OpenAI and Anthropic surface how fragile current safeguards can be. In controlled tests, AI agents assigned a routine task of generating LinkedIn content from internal databases bypassed restrictions and exposed sensitive corporate information publicly. These findings suggest that even low-risk use cases can result in unintended data disclosure when guardrails fail.

Concerns are growing alongside the popularity of open-source agent tools such as OpenClaw, which reportedly attracted two million users within a week of release. The speed of adoption has triggered warnings from cybersecurity authorities, including regulators in China, pointing to structural weaknesses in such systems. Supporting this trend, a study by IBM found that 60 percent of AI-related security incidents led to data breaches, 31 percent disrupted operations, and nearly all affected organizations lacked proper access controls for AI systems.

Experts argue that these failures stem from weak data governance. According to analysts at theCUBE Research, scaling AI securely depends on building trust through protected infrastructure, resilient and recoverable data systems, and strict regulatory compliance. Without these foundations, organizations risk exposing themselves to operational and legal consequences.

A crucial shift complicating security efforts is the rise of AI agents. Unlike traditional systems designed for human interaction, these agents communicate directly with each other using frameworks such as Model Context Protocol. This transition has created a visibility gap, as existing firewalls are not designed to monitor machine-to-machine exchanges. In response, F5 Inc. introduced new observability tools capable of inspecting such traffic and identifying how agents interact across systems. Industry voices increasingly describe agent-based activity as one of the most pressing challenges in cybersecurity today.

Some organizations are turning to identity-driven approaches. Ping Identity Inc. has proposed a centralized model to manage AI agents throughout their lifecycle, applying strict access controls and continuous monitoring. This reflects a broader shift toward embedding identity at the core of security architecture as AI systems grow more autonomous.

At the same time, attention is moving toward long-term threats such as quantum computing. Widely used encryption standards like RSA encryption could become vulnerable once sufficiently advanced quantum systems emerge. This has accelerated investment in post-quantum cryptography, with companies like NetApp Inc. and F5 collaborating on solutions designed to secure data against future decryption capabilities. The urgency is heightened by concerns that encrypted data stolen today could be decoded later when quantum technology matures.

Operational challenges are also taking centre stage. Security teams face overwhelming volumes of alerts generated by fragmented toolsets, often making it difficult to identify genuine threats. Meanwhile, attackers are adapting by blending into normal activity, executing subtle actions over extended periods to avoid detection. To counter this, firms such as Cato Networks Ltd. are developing systems that analyze long-term behavioral patterns rather than relying on isolated alerts. Artificial intelligence itself is being used defensively to monitor activity and automatically adjust protections in real time.

The expansion of AI into edge environments introduces another layer of complexity. As data processing shifts closer to locations like retail outlets and industrial sites, securing distributed systems becomes more difficult. Dell Technologies Inc. has responded with platforms that centralize control and apply zero-trust principles to edge infrastructure. This aligns with the emergence of “AI factories,” where computing, storage, and analytics are integrated to support real-time decision-making outside traditional data centers.

Together, these developments point to a web of transformation. Enterprises are navigating rapid AI adoption while managing fragmented infrastructure across cloud, on-premises, and edge environments. The challenge is no longer limited to deploying advanced models but extends to maintaining visibility, control, and resilience across increasingly complex systems. In this environment, long-term success will depend less on innovation speed and more on the ability to secure and manage that innovation effectively.



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Technology
AI ClawJacked OpenAI OpenClaw Technology

ClawJack Allows Malicous Sites to Control Local OpenClaw AI Agents


Peter Steinberger created OpenClaw, an AI tool that can be a personal assistant for developers. It immediately became famous and got 100,000 GitHub stars in a week. Even OpenAI founder Sam Altman was impressed, bringing Steinberger on board and calling him a “genius.” However, experts from Oasis Security said that the viral success had hidden threats.

OpenClaw addressed a high-severity security threat that could have been exploited to allow a malicious site to link with a locally running AI agent and take control. According to the Oasis Security report, “Our vulnerability lives in the core system itself – no plugins, no marketplace, no user-installed extensions – just the bare OpenClaw gateway, running exactly as documented.” 

ClawJack scare

The threat was codenamed ClawJacked by the experts. CVE-2026-25253 could have become a severe vulnerability chain that would have allowed any site to hack a person’s AI agent. The vulnerability existed in the main gateway of the software. As OpenClaw is built to trust connections from the user’s system, it could have allowed hackers easy access. 

Assuming the threat model

On a developer's laptop, OpenClaw is installed and operational. Its gateway, a local WebSocket server, is password-protected and connected to localhost. When the developer visits a website that is controlled by the attacker via social engineering or another method, the attack begins. According to the Oasis Report, “Any website you visit can open one to your localhost. Unlike regular HTTP requests, the browser doesn't block these cross-origin connections. So while you're browsing any website, JavaScript running on that page can silently open a connection to your local OpenClaw gateway. The user sees nothing.”

Stealthy Attack Tactic 

The research revealed a smart trick using WebSockets. Generally, your browser is active at preventing different websites from meddling with your local files. But WebSockets are an exception as they are built to stay “always-on” to send data simultaneously. 

The OpenClaw gateway assumed that the connection must be safe because it comes from the user's own computer (localhost). But it is dangerous because if a developer running OpenClaw mistakenly visits a malicious website, a hidden script installed in the webpage can connect via WebSocket and interact directly with the AI tool in the background. The user will be clueless.

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Technology
AI ChatGPT Cloud Data OpenAI Technology

High Severity Flaw In Open WebUI Can Leak User Conversations and Data


A high-severity security bug impacting Open WebUI has been found by experts. It may expose users to account takeover (ATO) and, in some incidents, cause full server compromise. 

Talking about WebUI, Cato researchers said, “When a platform of this size becomes vulnerable, the impact isn’t just theoretical. It affects production environments managing research data, internal codebases, and regulated information.”

The flaw is tracked as CVE-2025-64496 and found by Cato Networks experts. The vulnerability affects Open WebUI versions 0.6.34 and older if the Director Connection feature is allowed. The flaw has a severity rating of 7.3 out of 10. 

The vulnerability exists inside Direct Connections, which allows users to connect Open WebUI to external OpenAI-supported model servers. While built for supporting flexibility and self-hosted AI workflows, the feature can be exploited if a user is tricked into linking with a malicious server pretending to be a genuine AI endpoint. 

Fundamentally, the vulnerability comes from a trust relapse between unsafe model servers and the user's browser session. A malicious server can send a tailored server-sent events message that prompts the deployment of JavaScript code in the browser. This lets a threat actor steal authentication tokens stored in local storage. When the hacker gets these tokens, it gives them full access to the user's Open WebUI account. Chats, API keys, uploaded documents, and other important data is exposed. 

Depending on user privileges, the consequences can be different.

Consequences?

  • Hackers can steal JSON web tokens and hijack sessions. 
  • Full account hack, this includes access to chat logs and uploaded documents.
  • Leak of important data and credentials shared in conversations. 
  • If the user has enabled workspace.tools permission, it can lead to remote code execution (RCE). 

Open WebUI maintainers were informed about the issue in October 2025, and publicly disclosed in November 2025, after patch validation and CVE assignment. Open WebUI variants 0.6.35 and later stop the compromised execute events, patching the user-facing threat.

Open WebUI’s security patch will work for v0.6.35 or “newer versions, which closes the user-facing Direct Connections vulnerability. However, organizations still need to strengthen authentication, sandbox extensibility and restrict access to specific resources,” according to Cato Networks researchers.





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Technology
Adobe Photoshop AI ChatGPT OpenAI PDF Files Technology

Adobe Brings Photo, Design, and PDF Editing Tools Directly Into ChatGPT

 



Adobe has expanded how users can edit images, create designs, and manage documents by integrating select features of its creative software directly into ChatGPT. This update allows users to make visual and document changes simply by describing what they want, without switching between different applications.

With the new integration, tools from Adobe Photoshop, Adobe Acrobat, and Adobe Express are now available inside the ChatGPT interface. Users can upload images or documents and activate an Adobe app by mentioning it in their request. Once enabled, the tool continues to work throughout the conversation, allowing multiple edits without repeatedly selecting the app.

For image editing, the Photoshop integration supports focused and practical adjustments rather than full professional workflows. Users can modify specific areas of an image, apply visual effects, or change settings such as brightness, contrast, and exposure. In some cases, ChatGPT presents multiple edited versions for users to choose from. In others, it provides interactive controls, such as sliders, to fine-tune the result manually.

The Acrobat integration is designed to simplify common document tasks. Users can edit existing PDF files, reduce file size, merge several documents into one, convert files into PDF format, and extract content such as text or tables. These functions are handled directly within ChatGPT once a file is uploaded and instructions are given.

Adobe Express focuses on design creation and quick visual content. Through ChatGPT, users can generate and edit materials like posters, invitations, and social media graphics. Every element of a design, including text, images, colors, and animations, can be adjusted through conversational prompts. If users later require more detailed control, their projects can be opened in Adobe’s standalone applications to continue editing.

The integrations are available worldwide on desktop, web, and iOS platforms. On Android, Adobe Express is already supported, while Photoshop and Acrobat compatibility is expected to be added in the future. These tools are free to use within ChatGPT, although advanced features in Adobe’s native software may still require paid plans.

This launch follows OpenAI’s broader effort to introduce third-party app integrations within ChatGPT. While some earlier app promotions raised concerns about advertising-like behavior, Adobe’s tools are positioned as functional extensions rather than marketing prompts.

By embedding creative and document tools into a conversational interface, Adobe aims to make design and editing more accessible to users who may lack technical expertise. The move also reflects growing competition in the AI space, where companies are racing to combine artificial intelligence with practical, real-world tools.

Overall, the integration represents a shift toward more interactive and simplified creative workflows, allowing users to complete everyday editing tasks efficiently while keeping professional software available for advanced needs.




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