Researchers have revealed a new attack technique called VMScape that can break the security barriers between virtual machines and the systems that host them. This discovery is substantial because virtualization forms the backbone of today’s cloud computing environment, where multiple customers often share the same physical hardware.
How the attack works
Modern processors use a performance trick known as speculative execution, where the CPU guesses the next steps of a program before it is certain. While this speeds up computing, past incidents like the Spectre vulnerability have shown that attackers can manipulate this feature to gain access to protected information.
VMScape builds on this concept. Instead of targeting an individual application, it allows a malicious virtual machine to influence how the host hypervisor, the software that manages multiple virtual machines, makes predictions during execution. By carefully crafting these interactions, attackers can cause the hypervisor to briefly access secret data, such as encryption keys, which then leaves behind subtle traces in the processor’s memory cache. The attacker can measure these traces and piece together the stolen information.
The researchers focused on QEMU, a widely used hypervisor component. By training the processor’s branch prediction structures, a malicious VM can trick QEMU into speculatively executing instructions that leak information. To make the attack more reliable, the team developed methods to clear out cache entries and bypass protections like Address Space Layout Randomization (ASLR).
In practice, they managed to extract information at about 32 bytes per second with near-perfect accuracy. This means that a 4KB encryption key could be stolen in just over two minutes, while the full attack process, including defeating ASLR, took around 13 minutes.
Which systems are impacted
According to the findings, VMScape affects a wide range of AMD processors from the first Zen generation up to Zen 5, as well as Intel’s Coffee Lake CPUs. The latest Intel architectures, such as Raptor Cove and Gracemont, are not vulnerable. Importantly, the attack does not require altering the host system or disabling existing mitigations, making it more concerning for shared environments like public cloud platforms.
The implications for cloud security are clear: if one customer’s virtual machine can read sensitive data from another, it undermines trust in multi-tenant platforms. However, it is important to note that this attack is complex, requires expert-level skills, and demands uninterrupted time to execute. Ordinary users are unlikely to be directly affected.
Next steps
The discovery highlights the ongoing challenge of securing speculative execution in modern CPUs. While vendors are expected to release updates and mitigations, system administrators and cloud providers will need to stay alert and apply patches as they become available. For most users, the best course of action is to ensure their providers are following these security updates.
Cyberattacks are evolving fast, and one of the biggest threats on the horizon is ransomware that doesn't just take over your files but could directly attack your computer’s processor.
Usually, ransomware blocks access to your files or system until you pay money to get control back. But in the future, attackers might go deeper and mess with your computer’s central processing unit (CPU) — the part that controls everything your computer does.
This new kind of attack could change how your CPU works by tampering with a hidden set of instructions inside the chip, called microcode. These instructions are installed by companies like Intel and AMD and can only be updated by them. They help your CPU run smoothly and securely. If criminals figure out how to replace this microcode with harmful code, they could take over your computer entirely.
Although this might sound like science fiction, it's starting to become more real. Researchers recently found a way to insert custom code into an AMD processor by using a flaw. They managed to change how the CPU handles random numbers — a small change, but proof that deeper control is possible.
A cybersecurity expert from Rapid7 has even created a working example of this type of attack. While it's not being shared publicly, it shows that this type of threat may not be far off. Once such ideas are out in the open, it's only a matter of time before bad actors attempt to use them.
Some tools already exist that allow hackers to sneak malicious programs into the firmware — the part of your computer that runs before the operating system loads. These tools are sold online and used by cybercriminals to secretly gain access to computers.
Right now, there are no known real-world attacks that target the CPU in this way, and it may still be years before it becomes a serious problem. However, it’s smart to be prepared.
Here’s how you can reduce your risk:
1. Keep your BIOS and firmware updated regularly, since companies release updates to fix problems.
2. Use reliable antivirus software to catch other types of ransomware early.
3. Don’t open unknown emails or click suspicious links.
4. Only install programs from websites you trust.
While this type of ransomware isn't common today, the fact that it's possible means we should stay alert. Updating your system and being cautious online are simple steps that can go a long way in keeping your device safe.
Security researchers have uncovered two new vulnerabilities in modern Apple processors, named FLOP and SLAP, which could allow attackers to remotely steal sensitive data through web browsers. Discovered by researchers from the Georgia Institute of Technology and Ruhr University Bochum, these flaws exploit speculative execution, a performance optimization feature in Apple’s processors, to extract private user data from browsers like Safari and Chrome.
Speculative execution is a technique used by modern processors to predict and execute instructions in advance, improving performance. However, flaws in its implementation have led to significant security issues in the past, such as the Spectre and Meltdown attacks. FLOP and SLAP build on these exploits, demonstrating how Apple’s latest chips can be manipulated to leak private information.
FLOP (False Load Output Prediction) affects Apple’s M3, M4, and A17 processors. These chips attempt to predict not only which memory addresses will be accessed but also the actual data values stored in memory. If a misprediction occurs, the CPU may use incorrect data in temporary computations. Attackers can exploit this by measuring cache timing differences, allowing them to extract sensitive information before the system corrects itself. Researchers demonstrated FLOP by stealing private user data, including email details from Proton Mail, Google Maps location history, and iCloud Calendar events.
SLAP (Speculative Load Address Prediction) impacts Apple’s M2 and A15 processors, along with later models. Unlike FLOP, which predicts data values, SLAP manipulates the processor’s ability to anticipate which memory address will be accessed next. By training the CPU to follow a specific pattern and then suddenly altering it, attackers can force the processor to read sensitive data. The CPU processes this information before realizing the mistake, leaving traces that hackers can analyze. Researchers used SLAP to extract Gmail inbox content, Amazon order history, and Reddit activity.
Both FLOP and SLAP are particularly concerning because they can be executed remotely. A victim only needs to visit a malicious website running JavaScript or WebAssembly code designed to exploit these vulnerabilities. The attack does not require malware installation or direct access to the device, making it difficult to detect or prevent.
The researchers disclosed the flaws to Apple in early 2024. While Apple has acknowledged the issues, security patches have not yet been released. Apple has stated that it does not consider the vulnerabilities an immediate risk but has not provided a timeline for fixes. In the meantime, users concerned about potential data exposure can disable JavaScript in their browsers, though this may break many websites.
These findings highlight the growing sophistication of web-based attacks and the need for stronger security measures in modern processors. As Apple works on mitigating these vulnerabilities, users should stay informed about security updates and exercise caution when browsing unfamiliar websites.
The discovery of FLOP and SLAP underscores the ongoing challenges in securing modern processors against advanced exploits. While speculative execution enhances performance, its vulnerabilities continue to pose significant risks. As cyber threats evolve, both hardware manufacturers and users must remain vigilant, adopting proactive measures to safeguard sensitive data and maintain digital security.
As generative AI technology gains momentum, the focus on cybersecurity threats surrounding the chips and processing units driving these innovations intensifies. The crux of the issue lies in the limited number of manufacturers producing chips capable of handling the extensive data sets crucial for generative AI systems, rendering them vulnerable targets for malicious attacks.
According to recent records, Nvidia, a leading player in GPU technology, announced cybersecurity partnerships during its annual GPU technology conference. This move underscores the escalating concerns within the industry regarding the security of chips and hardware powering AI technologies.
Traditionally, cyberattacks garner attention for targeting software vulnerabilities or network flaws. However, the emergence of AI technologies presents a new dimension of threat. Graphics processing units (GPUs), integral to the functioning of AI systems, are susceptible to similar security risks as central processing units (CPUs).
Experts highlight four main categories of security threats facing GPUs:
1. Malware attacks, including "cryptojacking" schemes where hackers exploit processing power for cryptocurrency mining.
2. Side-channel attacks, exploiting data transmission and processing flaws to steal information.
3. Firmware vulnerabilities, granting unauthorised access to hardware controls.
4. Supply chain attacks, targeting GPUs to compromise end-user systems or steal data.
Moreover, the proliferation of generative AI amplifies the risk of data poisoning attacks, where hackers manipulate training data to compromise AI models.
Despite documented vulnerabilities, successful attacks on GPUs remain relatively rare. However, the stakes are high, especially considering the premium users pay for GPU access. Even a minor decrease in functionality could result in significant losses for cloud service providers and customers.
In response to these challenges, startups are innovating AI chip designs to enhance security and efficiency. For instance, d-Matrix's chip partitions data to limit access in the event of a breach, ensuring robust protection against potential intrusions.
As discussions surrounding AI security evolve, there's a growing recognition of the need to address hardware and chip vulnerabilities alongside software concerns. This shift reflects a proactive approach to safeguarding AI technologies against emerging threats.
The intersection of generative AI and GPU technology highlights the critical importance of cybersecurity in the digital age. By understanding and addressing the complexities of GPU security, stakeholders can mitigate risks and foster a safer environment for AI innovation and adoption.