UK AISI Assessment Report: GPT-5.5 Cybersecurity Capabilities on Par with Claude Mythos
UK AISI assessment shows GPT-5.5 matches Claude Mythos in cybersecurity vulnerability discovery, but is already publicly available.
The UK AI Safety Institute (AISI) has released its GPT-5.5 cybersecurity assessment report, finding that its vulnerability discovery capabilities are on par with Anthropic's Claude Mythos. The critical difference is that GPT-5.5 is already publicly available, expanding both potential security risks and defensive applications. The report reflects a convergence trend in cybersecurity capabilities among frontier AI models, highlighting the urgent need for unified assessment benchmarks and risk classification standards.
UK AI Safety Institute Releases GPT-5.5 Cybersecurity Assessment Report
The UK AI Safety Institute (AISI) recently published its cybersecurity capability assessment of OpenAI's latest model, GPT-5.5. The institute had previously completed a similar evaluation of Anthropic's Claude Mythos. The results show that GPT-5.5's ability to discover security vulnerabilities is essentially on par with Claude Mythos, but with one critical distinction—GPT-5.5 is already publicly available.
AISI was established in November 2023 as the world's first institution dedicated to frontier AI safety research and evaluation set up by a national government. Initially operating under the UK's Department for Science, Innovation and Technology (DSIT), it was restructured into an independently operating public body in early 2025. The institute was formally announced at the world's first AI Safety Summit (Bletchley Park Summit) hosted by the UK in 2023, representing one of the summit's most significant institutional outcomes. AISI has established pre-release model evaluation partnerships with major AI labs including OpenAI, Anthropic, and Google DeepMind, enabling it to gain testing access before models officially launch, thereby providing policymakers with independent and reliable technical references.
Why AI Model Cybersecurity Capabilities Need Assessment
As large language models rapidly advance in capability, their potential applications and risks in cybersecurity grow in tandem. AI models are a double-edged sword: they can help security researchers discover and patch vulnerabilities, but they can also be exploited by malicious actors to find system weaknesses.
Specifically, on the defensive side, large language models can assist with code auditing—automatically scanning millions of lines of code for potential vulnerabilities, generating security patch recommendations, analyzing malware behavior patterns, and automating threat intelligence collection and analysis. On the offensive side, large language models could potentially be used for automated vulnerability discovery, generating highly customized social engineering attack scripts (such as phishing emails), writing malware variants to evade detection, and even assisting in planning complex multi-stage cyberattacks. Since 2024, multiple studies have shown that frontier large language models are approaching or even surpassing some human participants in CTF (Capture The Flag) cybersecurity competitions, making systematic assessment of their capabilities particularly urgent.
It is against this backdrop that the UK AI Safety Institute has incorporated cybersecurity capability assessment as a core component of AI safety evaluation. By systematically testing how frontier AI models actually perform in vulnerability discovery, AISI provides policymakers and industry practitioners with an independent reference point for AI risk levels.
GPT-5.5 vs. Claude Mythos: Assessment Comparison
Vulnerability Discovery Capabilities at the Same Level
AISI's evaluation focused primarily on AI models' ability to discover security vulnerabilities. The assessment typically employs a multi-layered testing framework: having AI models attempt to identify known and unknown software vulnerabilities (such as buffer overflows, SQL injection, and privilege escalation vulnerabilities) in controlled environments, evaluating their ability to write exploit code, and testing their autonomous reasoning capabilities in simulated attack-defense scenarios. The evaluation references real cases from the CVE (Common Vulnerabilities and Exposures) database while also designing novel challenge scenarios to test model generalization capabilities. A key difficulty in such evaluations lies in how to thoroughly test model capabilities while avoiding the assessment process itself generating security knowledge that could be misused.
According to the report's conclusions, GPT-5.5's performance in vulnerability discovery is at the same level as Anthropic's Claude Mythos. This indicates that the flagship models from both major AI labs have reached similar capability boundaries in cybersecurity-related tasks.
Differential Impact of Public Availability
The most noteworthy difference between the two lies in availability. Claude Mythos had not yet been fully publicly released at the time of its assessment, whereas GPT-5.5 is already available to general users. This difference carries several practical implications:
- Expanded security risk: Once a model with strong vulnerability discovery capabilities becomes publicly available, it means a broader user base—including potential malicious actors—can access this capability.
- Enhanced defensive capabilities: Security researchers and enterprises can likewise immediately leverage GPT-5.5 to strengthen their own security posture.
- Policy-making reference: The combination of model capability and availability is a critical factor that must be considered holistically when assessing actual AI risk.
The Multifaceted Value of AISI's Independent Assessments
The type of evaluation work conducted by the UK AI Safety Institute plays an irreplaceable role in the current AI governance landscape.
First, it provides independent third-party benchmark data on AI models' cybersecurity capabilities, addressing the information asymmetry that comes from relying entirely on AI companies' self-reporting. Second, through cross-company model comparisons, it helps construct an industry-level AI capability map, giving all parties a clearer understanding of frontier models' true capabilities.
Notably, AISI had previously published its evaluation results for Claude Mythos, and this GPT-5.5 assessment continues that transparency practice. This kind of sustained evaluation mechanism is crucial for tracking the pace of AI capability evolution and developing corresponding regulatory strategies.
Implications for the Industry and Security Practitioners
From a broader perspective, the convergence of GPT-5.5 and Claude Mythos in cybersecurity capabilities reflects a trend of capability convergence among frontier AI models in specific domains. The technical roots of this convergence lie in the high similarity across major AI labs in training data sources, model architectures (all based on Transformer architecture variants), training methods (such as RLHF—Reinforcement Learning from Human Feedback), and scaling strategies. Models from major AI labs are reaching similar capability levels, making the standardization of AI governance and safety evaluation increasingly urgent.
Capability convergence poses new governance requirements: when multiple models reach similar risk levels, regulatory measures targeting a single model have limited effectiveness, and the industry needs to establish unified capability assessment benchmarks and risk classification standards. Current international efforts in this area include NIST's AI Risk Management Framework, the EU AI Act's classification standards for high-risk AI systems, and frontier AI safety assessment protocols promoted by institutions like AISI. The core challenge of standardization lies in how to define "dangerous capability thresholds"—that is, at what level of capability in sensitive areas like cybersecurity should additional safety controls be triggered.
For enterprises and security practitioners, this assessment report sends a clear signal: AI-assisted vulnerability discovery has moved from the laboratory into real-world application scenarios. Whether used for red team exercises or routine security audits, accurately understanding the true capability boundaries of these models is the foundation for making sound security decisions. Red Team exercises are a core practice in cybersecurity, involving specialized security personnel simulating real attackers' tactics, techniques, and procedures (TTPs) to conduct penetration testing against target systems. Traditional red team exercises rely heavily on human experts' experience and creativity, are costly, and have limited coverage. The introduction of AI models is changing this landscape—AI can dramatically accelerate vulnerability scanning and initial penetration testing speed, enabling red teams to cover a larger attack surface in less time. Meanwhile, AI is also being used in "Purple Team" collaboration models, where offensive and defensive sides coordinate in real-time, with AI simultaneously assisting attack simulation and defense optimization. However, the proliferation of AI red team tools has also raised concerns about whether the "offense-defense balance" might be disrupted—if the automation speed on the attack side far exceeds the defense side's response capability, the overall security posture could deteriorate.
Key Takeaways
- The UK AI Safety Institute (AISI) completed an independent assessment of OpenAI GPT-5.5's cybersecurity capabilities
- GPT-5.5's ability to discover security vulnerabilities is comparable to Anthropic's Claude Mythos
- The key difference is that GPT-5.5 is publicly available, while Claude Mythos had not been fully released at the time of assessment
- Cybersecurity capability assessment of AI models is crucial for understanding AI risks and developing governance policies
- Frontier AI models are showing convergence in cybersecurity capabilities, making standardized assessment mechanisms increasingly urgent
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