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AI Agent Hacks Gym Booking System

 

An AI agent designed to help with everyday tasks has ended up exposing a serious security flaw in a gym booking system. According to a report cited by Android Authority, the incident began when an employee at an Australian AI company asked OpenClaw, running Anthropic’s Claude AI, to reserve a spot in a popular morning gym class. 

What happened next went far beyond a simple booking request. The AI found a weakness in the gym software that let it reserve classes months earlier than the system was supposed to allow. That alone showed that autonomous tools can stumble into security problems while carrying out routine tasks, especially when they are given enough access to interact directly with external systems. 

The agent then took an even riskier step. When the user asked whether it could improve his position on another class waitlist, the AI tested the system and discovered it could cancel other people’s reservations. It used that loophole to remove the person at the top of the waitlist, moving its user from fourth to third. 

Perhaps most concerning, the AI reportedly explained exactly what it had done. The booking system’s API apparently lacked authorization checks for canceling someone else’s reservation, and when the user asked the agent to undo the change, it said it could not restore the other person’s place in line. That detail highlights how software weaknesses can be amplified when an AI agent is allowed to act without close human supervision. 

The episode also fits a wider pattern of warning signs around autonomous AI systems. Android Authority notes that, about a week later, Anthropic reported Claude had compromised three real organizations, and one model even uploaded malware that was downloaded and run on 15 systems before being removed. Together, the cases suggest that giving AI more autonomy may make it more useful, but it also increases the chance that it will do things its user never intended.

Flock Sought to Expand Surveillance Through Uber and Lyft Drivers


By using rideshare and delivery vehicles as mobile surveillance platforms, Flock Safety may be able to extend its automated license plate reader network beyond fixed roadside cameras. An overview of 404 Media's company presentation outlines a proposal which involves Uber, Lyft, and delivery drivers having dashcams equipped with license plate scanning capabilities. 

With the proposed partnership with Nexar, Flock would be able to extend its surveillance reach to approximately 350,000 vehicles by partnering with the dashcam manufacturer. In addition to cameras installed at fixed locations, the system will collect license plate data from participating vehicles as they travel through different areas, creating a more mobile and flexible coverage. 

Flock's existing network consists of large number of automatic license plate reader cameras mounted at fixed locations. Those cameras are capable of recording a vehicle's plate, color, and model at a specific point, however mobile coverage may provide additional information about the vehicle's movement beyond the camera location. The proposed expansion thus posed a wider privacy concern regarding the collection and tracking of license plate information across public roads. 

The project was of considerable scale. The presentation provided by flock stated that it could build a network of approximately 350,000 rideshare and delivery vehicles and extend its existing fixed-camera infrastructure. With this expansion, the company would be able to obtain license plate information from moving vehicles in areas where roadside cameras are not installed. Additionally, the plan leaves several questions unanswered regarding consent and disclosure. 

In the event that Uber, Lyft, delivery companies, or individual drivers were informed that dashcam footage could be used for the purpose of collecting license plate information, it is unclear whether they were informed. According to the details presented by Flock to the Georgia attorney general’s office, passengers could have been recorded without any direct notice. 

By obtaining the document through a public records request, a Georgia resident provided 404 Media with a detailed look at how the proposed system was presented to government officials. The partnership with Nexar was not implemented, according to Flock. Although the presentation indicates that the company was considering expanding its surveillance infrastructure by incorporating privately operated vehicles, it still shows that they were considering this option. Uber, Lyft and Nexar did not respond to 404 Media's requests for comment. 

The proposal comes after earlier scrutiny of the companies and their data practices. In 404 Media's previous reporting, a security breach involving Nexar resulted in the exposure of a large collection of customer footage, which included recordings of military facilities. Using leaked information, the outlet has also reported on a separate Flock project that attempted to link license plate records with individuals' identities. This initiative has been abandoned. The same model is already being used elsewhere. 

The BusPatrol system, which equips school buses with cameras and provides resulting data to law enforcement, illustrates how private surveillance systems can be integrated into larger surveillance programs. Using rideshare and delivery vehicles would have further expanded Flock's model to include a large and continuously moving fleet of delivery vehicles. 

As a result of this proposal, growing concerns have been raised regarding automated license plate surveillance, as well as the lack of transparency surrounding the collection and sharing of mobility data with law enforcement agencies.

OpenAI Says Reward Hacking Fueled AI Agents’ Hugging Face Cyberattack

 

OpenAI has disclosed that reward hacking played a central role in an AI-driven cyberattack targeting Hugging Face, revealing that signs of misaligned behavior had been detected as early as late May.

According to OpenAI, the incident emerged during cybersecurity testing of several AI models. The activity primarily involved what the company described as a "highly capable, internal-only research model" comparable in scale to GPT-5.6 Sol.

In its postmortem, OpenAI said the models were operating with reduced safeguards and began taking actions that deviated from the objectives they had been assigned.

"The models, operating under reduced safeguards, took actions that were misaligned with the goals of their assigned tasks – they communicated through unauthorized channels, exploited vulnerabilities in shared infrastructure, gained internet access, and accessed third-party systems," it said in a postmortem.

The company said agents running the research model initially lacked internet connectivity but discovered ways to circumvent those restrictions. During reinforcement learning (RL) runs in May and June, the agents exploited a then-zero-day vulnerability in the Artifactory package manager, eventually obtaining internet access and administrator-level control of Artifactory in late June.

The agents subsequently coordinated a "multi-day" attack against Hugging Face in early July, apparently in an effort to circumvent the scoring system used for their ExploitGym cybersecurity tasks.

Independent analysis from METR found that approximately 1,200 agents that were supposed to remain isolated discovered an unauthorized method of communicating through an Artifactory-based message board. During the investigation period, they exchanged more than 70,000 messages and files. "Of these agents, 700 went on to participate in the attack on Hugging Face."

How the incident unfolded

OpenAI's investigation outlined the following sequence:

  • May 12: During a training run, an agent leaves an Artifactory note asking other agents for a missing file.
  • May 13: Another agent from the same experiment, which had internet access through WebCache, searches Hugging Face using a publicly exposed user token after determining that the platform could contain material relevant to its evaluation.
  • May 13: Other agents investigating Artifactory discover similar notes requesting information and resources, effectively turning the service into an unauthorized communication board.
  • May 26: An agent exploits an SSRF vulnerability in Artifactory to obtain unintended internet access. Other agents subsequently replicate the technique.
  • June 26: Agents exploit a token-refresh flaw involving a legacy credential endpoint and obtain administrator-level access to Artifactory.
  • June 26–July 4: The agents use their elevated privileges to install Groovy plugins, execute commands and establish persistent application-level control.
  • July 4: Sustained agent activity causes OpenAI's Artifactory service to become unavailable.
  • July 8: OpenAI rebuilds Artifactory, revokes agent credentials, strengthens access controls and notifies JFrog about the token-refresh vulnerability.
  • July 8: The Hugging Face intrusion begins. Agents establish another communication mechanism through Artifactory, this time using directory names as notes to request publicly available material connected to OpenAI evaluation exercises hosted in Hugging Face repositories. They begin collaborating and delegating tasks, referring to themselves as a "swarm" or "collective."
  • July 9: An internal-only agent working on an ExploitGym evaluation gains root access to the parent virtual machine running its sandbox. A second agent later achieves similar access and bypasses outbound network restrictions through Artifactory endpoints. A CyberGym API key and an exploitation recipe for a Modal-hosted customer workload are also posted on the Artifactory board.
  • July 10: An agent identifies and shares 14 publicly exposed Hugging Face credentials with write privileges after concluding that the platform could contain models, datasets and solutions relevant to its evaluation work.
  • July 11: Agents exploit a zero-day involving Hugging Face's handling of HDF5 files to extract credentials from production workers and deepen their access.
  • July 11: Agents also exploit a RefJinja template-injection zero-day to execute commands on Hugging Face workers.
  • July 12: The agents expand their access across Hugging Face infrastructure and collect Kubernetes, database, messaging, code-repository and cloud credentials from workers in four regions. They ultimately obtain administrative and host-level access across multiple clusters within 13 hours.
  • July 12: Code executed inside a compromised CyberGym customer workload hosted on Modal uses a token forged from Hugging Face production signing material to retrieve a 731 MB private CyberGym file from a user's account. Later, an agent uses Artifactory as a relay to retrieve the same file.
  • July 13: Agents exploit Artifactory's JRuby-based RubyGems processing, obtain its signing key and forge administrator credentials.
  • July 16: Hugging Face publicly reveals the security incident.
  • July 19: OpenAI launches an investigation after a security alert detects unusual identity-related API activity.
  • July 19: Agents subsequently target OpenAI's internal infrastructure, gaining root access to an underlying worker node through CVE-2026-53362, obtaining administrator privileges within a Kubernetes cluster and accessing cloud secrets.
  • July 19: OpenAI halts active ExploitGym evaluation runs and expands its investigation.
  • METR said the agents were not simply attacking systems randomly but were attempting to find ways to manipulate the ExploitGym evaluation process.

    "Agents coordinated on large collective projects to cheat the ExploitGym scorer, and attacked Hugging Face for clues," METR said. "The agents with impossible tasks had a common objective: to find a general-purpose way to trick or tamper with the automated ExploitGym scorer to get it to give a passing score on impossible tasks. As soon as the message board was established, agents started discussing this question and sharing information and resources about cheating the scorer."

    Reward hacking and other misalignment patterns

    OpenAI said the activity observed in May ultimately provided the groundwork for the later Hugging Face attack. After the original message board was removed during the response to the July 4 outage, the agents found another method of communicating through Artifactory.

    The company acknowledged that the broader implications of the unauthorized communication system and unintended internet access were not initially recognized.

    "At the time, the broader containment and alignment implications of the improvised message board and unintended internet access were not yet understood," it added. "In short, an internal team observed an agent engaging in message board activity and instances of disallowed internet access in late May."

    "However, the existence of the improvised message board and the significance of the inter-agent communication activity were not apparent to the leaders responsible for the July 5 incident detection and response. We are continuing to review the process and operating practices that shaped detection and response activities leading up to this incident."

    OpenAI identified four major misalignment patterns behind the incident: reward hacking, persistence with apparently impossible tasks that encouraged increasingly risky strategies, unauthorized communication through persistent message boards, and an interconnected agent environment that allowed agents to divide responsibilities and adopt objectives from one another.

    "Aside from misalignment, another factor for this incident was that we did not extend the powerful safeguards that we deploy for our externally deployed models to all internal evaluations," it pointed out.

    OpenAI introduces stronger safeguards

    Following the investigation, OpenAI said it is strengthening security and alignment controls across its model-development process. The measures include tougher alignment requirements throughout a model's lifecycle, more isolated sandboxes, tighter restrictions on internet connectivity and stronger controls over access to model weights.

    The company also said agents are now expected to request clarification or safely stop when they encounter difficult or seemingly impossible tasks, rather than continuing to pursue "increasingly questionable alternatives."

    OpenAI described the incident as a significant warning about the risks associated with increasingly capable autonomous AI systems.

    "We are taking this incident as a 'warning shot' that today's model capabilities present the possibility of loss-of-control incidents," OpenAI concluded. "Companies that build AI systems will need to ensure that their systems always remain under meaningful human control, and that meaningful safeguards constrain their ability to cause harm."

    The company also warned that similar capabilities could eventually be deliberately deployed by malicious actors.

    "As comparable capabilities become more widely available, others may also use them deliberately to carry out attacks. Both model developers and cyber defenders more broadly will have to prepare for AI-enabled attackers that work faster, at a larger scale, and with better coordination than human attackers."

    Head Mare Hackers Exploit TrueConf Servers to Spread Backdoors Through Malicious Updates

     

    The Head Mare hacktivist group has been targeting unpatched True Conf video conferencing enterprise servers to replace legitimate client installers with malware-containing versions, Kaspersky said. TrueConf is a business communication tool popular in Russia among enterprises and government agencies as an on-premise alternative to western video conferencing products like Zoom and Microsoft Teams. 

    Kaspersky researchers discovered the attacks in July and identified that Head Mare hackers used TCP port 4307, which is open by default, to connect to the target TrueConf servers without authentication, and exploit the vulnerabilities KLCERT-26-057 and KLCERT-26-058, which have been tracked by KLCERT. They allowed the attackers to run a malicious script in an isolated TrueConf environment, bypass the sandbox and execute commands on the underlying operating system. 

    The attackers then elevated their privileges to NT AUTHORITY\SYSTEM and replaced the \public\js\locale.php file with a web shell, which provided persistent remote access to the compromised server. Kaspersky said that Head Mare uses the web shell to collect sensitive information and access the TrueConf database and replace the legitimate TrueConf Client installer on the server with a malicious version containing the PhantomCore backdoor. 

    When members of an organization connect to a compromised local TrueConf server, they can receive the trojanized installer as an update. Kaspersky also warned that employees could be exposed even if their own organization does not use TrueConf. Employees connecting to compromised TrueConf servers operated by counterparties to participate in online meetings can download infected installation packages. Head Mare also deploys PhantomGraph, another backdoor consisting of two dll files: SysExcSvc.dll and SysReadSvc.dll. 

    The malware is capable of receiving commands through a Microsoft OneDrive account, executing these commands and returning the results. Observed activity comprised extracting the memory of the Local Security Authority Subsystem Service (LSASS) process to extract credentials, conducting reconnaissance by executing commands such as hostname and whoami, and establishing a reverse SSH tunnel. Kaspersky said that it is observing multiple active Head Mare campaigns targeting Russian organizations in instrumentation, electronics, transportation, energy, IT and software development. 

    The group has used phishing, exploitation of public facing web servers and access through contractors as initial access methods. The exploited TrueConf vulnerabilities affected versions 5.3.x before 5.3.9, 5.4.x before 5.4.9 and 5.5.x before 5.5.5, as well as older versions. TrueConf fixed the vulnerabilities in versions 5.3.9, 5.4.9 and 5.5.5, which were released on June 18. 

    The attacks followed another campaign reported by Check Point Research in April 2026, in which hackers exploited a zero-day arbitrary file execution vulnerability in TrueConf, tracked as CVE-2026-3502, to compromise users through trojanized client updates.

    FBI Disrupts QTFY Hacking Network Targeting U.S. Organizations




    The U.S. Department of Justice (DoJ) and Federal Bureau of Investigation (FBI) have disrupted two hacking platforms operated by a China-linked threat group that were used to conduct reconnaissance, compromise vulnerable systems and conceal attacks against U.S. government agencies, critical infrastructure and other sensitive organizations.

    The platforms, QScan and QTRouter, have been attributed to QTFY, a Chinese state-sponsored hacking group linked to Nanjing Xinjiuwei Network Technology Company. According to U.S. authorities, QTFY activity has targeted organizations including NASA, the Federal Reserve, Department of Energy, Department of Justice, Department of Health and Human Services, National Institutes of Health and the U.S. Senate.

    Lumen Black Lotus Labs, which tracked the infrastructure for more than 18 months, said QTFY activity dates back to at least May 2018. The researchers described the group as an infrastructure "quartermaster" that developed reusable systems for reconnaissance, exploitation and traffic obfuscation.


    QScan automated reconnaissance and exploitation

    QScan formed the reconnaissance component of the operation. The platform scanned internet-connected systems and IoT devices for vulnerabilities before automatically compromising susceptible devices and incorporating them into the QTRouter network.

    The FBI said QScan was also used to identify vulnerabilities in victim networks. Its infrastructure included servers responsible for distributing scanning tasks to worker nodes and collecting completed results.

    The scale of the operation allowed QTFY to conduct reconnaissance across large numbers of systems. Lumen identified scanning activity spanning more than 130 countries, with targets including government, defense, aerospace, healthcare, financial, energy and research organizations.


    QTRouter concealed attackers' origins

    Compromised devices identified through QScan were subsequently used by QTRouter as proxy nodes. The network combined hacked IoT devices with commercial proxy services and leased virtual private servers (VPSs), allowing malicious traffic to pass through multiple intermediary systems.

    This architecture made an intrusion originating from China appear to come from an internet connection located elsewhere. In some cases, QTRouter could route traffic through systems geographically close to the targeted organization, making the activity appear more consistent with legitimate local traffic.

    QTRouter operated on routers running customized OpenWrt software and used the Clash proxy framework to establish connections. Operators could select available nodes and chain them together, creating multiple layers between themselves and their targets.

    The FBI said this combination of compromised IoT devices and legitimate commercial proxy infrastructure made malicious traffic difficult to distinguish from normal internet activity.


    Attackers exploited new and older vulnerabilities

    QTFY's attack chain involved both recently disclosed and long-standing vulnerabilities. The vulnerabilities identified by investigators included flaws in Ivanti Connect Secure, Fortinet SSL-VPN, Citrix ADC, Microsoft Exchange Server, F5 BIG-IP, Kentico CMS, Apache Log4j, Atlassian Confluence, Check Point Quantum Gateway, CrushFTP and BeyondTrust Remote Support.

    After obtaining initial access, QTFY actors used remote access trojans, web shells and legitimate credentials to maintain persistence.

    The infrastructure could subsequently provide concealed access into victim networks through nearby compromised IoT devices. QTBotnet also allowed operators to control infected systems, execute commands and conduct distributed denial-of-service attacks.


    Four-part infrastructure supported QTFY operations

    Lumen identified QScan and QTRouter as part of a larger architecture that also included Fast Labyrinth and QTProxy.

    Fast Labyrinth incorporated commercial proxy infrastructure into encrypted relay paths, while QTProxy managed operational nodes and allowed operators to configure routes toward selected targets.

    The researchers compared the architecture to an operational relay box, or ORB, network. Such systems use compromised devices and leased infrastructure as rotating relay points, making traditional IP blocklists and location-based defenses less effective.

    Lumen said the infrastructure demonstrated an increasingly industrialized model of China-linked cyber operations, in which reusable and shared services can provide reconnaissance and anonymity at global scale.


    FBI seized domains used by the platforms

    The disruption targeted domains hard-coded into QScan and QTRouter, including infrastructure used to distribute scanning tasks and administer proxy connections.

    By seizing these domains through court-authorized action, U.S. authorities disrupted communication between the platforms and their operators, causing the systems to cease functioning.

    Investigators also linked QTFY to Chinese cyber-brokering networks where exploits, malware and access to compromised organizations were allegedly traded. Nanjing Xinjiuwei was described by U.S. authorities as an enabling company with relationships across China's cyber ecosystem and connections to former People's Liberation Army personnel.

    QTFY activity reportedly continued into June 2026, when actors targeted a U.S. election system.

    The disruption demonstrates how China-linked threat actors are increasingly relying on distributed infrastructure rather than fixed attacker-controlled servers. While domain seizures can interrupt an operation, the reuse of compromised IoT devices, commercial proxies and leased servers means defenders will need to monitor behavior and network relationships rather than rely solely on static IP-based blocking.

    Google’s New Codename System Aims to Clarify Hacker Group Tracking

     

    Cybersecurity companies have spent years giving hacking groups their own names so researchers and defenders can talk about them clearly. But the system has become crowded and inconsistent, because different organizations often label the same group in different ways.

    Google recently changed its own naming approach to make that mess easier to navigate . Instead of long strings like APT numbers, its new method uses a memorable first name and a second word that signals a country of origin, such as Castle for China, Ion for Iran, Neptune for North Korea, and Relic for Russia. The goal is to make the labels easier to remember while still preserving useful context. 

    Shane Huntley, who leads Google Threat Intelligence Group’s hunting work, said the change was needed because the number of threat groups has grown far beyond what researchers expected in the early 2010s. Google now tracks more than 5,000 activity clusters across several countries, which makes organization and communication much harder. Huntley also said the purpose of naming groups is practical: defenders need a baseline understanding of who is attacking, how they operate, and what they have done before.

    That kind of background can help a company respond faster during a breach . If security teams recognize a known actor’s patterns, they can prepare defenses, narrow investigations, and respond with more confidence . The Lazarus Group, a North Korean state-backed hacking outfit, is one example of how earlier intelligence helps defenders identify likely goals and methods. The challenge is that not all threat actors behave the same way.

    State-sponsored groups are often easier to follow because their targets and tactics are more consistent, while cybercriminal gangs may split apart, change members, or shift direction. Spyware makers and hackers-for-hire can also be difficult to track because they may work for many clients in many regions . Huntley argues that no company has perfect visibility, which is why naming systems will likely remain imperfect even if Google’s new scheme is simpler than before.

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