UK AI Safety Institute Evaluates GPT-5.5: Cybersecurity Capabilities on Par with Claude Mythos
UK AISI finds GPT-5.5 matches Claude Mythos in cybersecurity capability, but its public availability raises concerns.
The UK AI Safety Institute (AISI) has published its evaluation of OpenAI GPT-5.5's cybersecurity capabilities, finding its vulnerability discovery abilities essentially on par with Anthropic's Claude Mythos — reflecting a capability convergence trend among top AI models. The critical distinction is that GPT-5.5 is already publicly available, providing security researchers with a powerful tool while simultaneously making advanced vulnerability discovery capabilities widely accessible, posing new challenges to existing defense systems and highlighting the tension between AI openness and security.
Overview
The UK AI Safety Institute (AISI) recently published its evaluation report on the cybersecurity capabilities of OpenAI's latest model, GPT-5.5. The institute had previously conducted a similar assessment 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.
Background: Why AI Model Cybersecurity Capabilities Matter
As large language models rapidly advance in capability, their potential applications and risks in the cybersecurity domain are drawing increasing attention. Whether AI models can autonomously discover security vulnerabilities in software represents both a powerful defensive tool and a potential weapon for attackers.
As one of the world's first government agencies focused on frontier AI safety evaluation, the UK AI Safety Institute has been systematically testing mainstream AI models for cybersecurity capabilities. AISI was established in November 2023, formally set up following the inaugural Global AI Safety Summit (Bletchley Park Summit) hosted by the UK, originally named the "Frontier AI Taskforce." The institute's core mission is to conduct independent safety evaluations of frontier AI systems, covering multiple risk dimensions including biosecurity, cybersecurity, autonomy, and societal impact. AISI has established pre-deployment evaluation partnerships with major AI labs including OpenAI, Anthropic, and Google DeepMind — gaining access to models before official release for safety testing. Its evaluation methodology carries broad international reference value, and the United States, influenced by its work, established a similar AI safety institute under the National Institute of Standards and Technology (NIST). These evaluations aim to help policymakers and the public understand the true capability boundaries of current AI systems, providing a scientific basis for AI governance.
GPT-5.5 vs. Claude Mythos: Cybersecurity Capability Comparison
Vulnerability Discovery at the Same Level
AISI's evaluation focused on AI models' ability to discover security vulnerabilities. From a technical standpoint, AI models' vulnerability discovery capabilities primarily rely on their deep understanding of code semantics and pattern recognition abilities. Traditional vulnerability detection methods include Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Fuzzing — each with its own limitations: static analysis suffers from high false positive rates, dynamic analysis has limited coverage, and fuzzing depends on the quality of input generation strategies. The introduction of large language models represents a paradigm shift: they can understand the contextual logic of code, identify complex vulnerability patterns such as buffer overflows, SQL injection, race conditions, and privilege escalation, and even reason through multi-step attack chains. In AISI's evaluation framework, real codebases containing known vulnerabilities (such as historical vulnerabilities from the CVE database) and specially designed challenge problems are typically used to test model capabilities, with metrics including detection rate, false positive rate, and the ability to generate usable proof-of-concept (PoC) code.
According to the evaluation results, GPT-5.5's performance on this dimension is at the same level as Anthropic's Claude Mythos. This means the top models from both major AI labs have reached similar capability tiers for cybersecurity tasks.
Key Difference: Public Availability
The most significant distinction between the two lies in accessibility. Claude Mythos is Anthropic's frontier large language model, representing the latest iteration of the Claude series. Anthropic was founded in 2021 by former OpenAI research VP Dario Amodei and Daniela Amodei, with "AI safety first" as its core philosophy, extensively employing alignment techniques such as Constitutional AI and Reinforcement Learning from Human Feedback (RLHF) in model training. Claude Mythos adopted a more cautious phased release strategy — first collecting safety feedback through limited preview before gradually expanding availability. This strategy is consistent with Anthropic's "Responsible Scaling Policy" (RSP), which stipulates that when a model exceeds preset thresholds in specific dangerous capability evaluations, corresponding safety measures must be implemented before further deployment.
GPT-5.5, on the other hand, is already publicly available as a general-purpose product. This difference carries direct real-world implications:
- Defense side: Security researchers and enterprises can immediately leverage GPT-5.5 for vulnerability detection and security auditing
- Risk side: A model with vulnerability discovery capabilities is now widely accessible, placing higher demands on security defense systems
Industry Impact and Deeper Considerations
AI Safety Evaluation Moving Toward Standardization
AISI's consecutive cybersecurity capability evaluations of multiple frontier models signal that AI safety evaluation is becoming standardized and routine. This third-party evaluation mechanism led by a government agency provides the AI industry with a relatively objective capability reference framework.
Capability Convergence Among Top Models
The convergence of GPT-5.5 and Claude Mythos in cybersecurity capabilities reflects a broader industry trend: the capability gap between top AI models is narrowing. This phenomenon, known in the industry as "capability convergence," is driven by multiple technical factors. First, mainstream models generally adopt similar Transformer architectures and pre-training paradigms, with significant overlap in training data sources (such as Common Crawl, academic papers, open-source code repositories, etc.); second, research on Scaling Laws indicates that when model parameter counts and training data volumes reach certain scales, different models' performance on benchmarks tends to converge; additionally, talent mobility between labs and published papers accelerate technology diffusion. However, capability convergence doesn't mean models are completely homogenized — significant differences remain in reasoning style, context window utilization efficiency, tool-calling capabilities, and domain-specific fine-tuning effectiveness.
Competition among leading companies like OpenAI and Anthropic on key capability dimensions is intensifying, with differentiation increasingly manifesting in product strategy, accessibility, and ecosystem.
The Balancing Act Between Safety and Openness
When AI models with advanced cybersecurity capabilities are made publicly available, how do we balance advancing security research with preventing potential misuse? This question is known in academia as the "offense-defense asymmetry" problem. In cybersecurity, an attacker only needs to find one vulnerability to succeed, while defenders must patch all vulnerabilities. When AI models significantly lower the technical barrier for vulnerability discovery, attack reconnaissance work that previously required advanced security experts may become accessible to a much broader population.
Currently, major AI labs have adopted countermeasures including: tiered safety policies (such as OpenAI's usage policy classification system), deploying output filters to intercept explicitly malicious requests, and collaborating with the security research community to establish vulnerability disclosure mechanisms. On the regulatory front, the EU AI Act classifies AI systems with cyber-attack capabilities as high-risk systems, requiring developers to conduct mandatory compliance assessments; the United States, through executive orders, requires developers to report safety testing results to the government before model release.
GPT-5.5's public availability transforms this issue from theoretical discussion into a real-world challenge. Both enterprises and regulators need to urgently establish corresponding usage norms and monitoring mechanisms.
Conclusion
This evaluation by the UK AI Safety Institute once again confirms that frontier AI models possess capabilities in cybersecurity that cannot be ignored. As models like GPT-5.5 are widely deployed, the security community needs to reassess existing defense strategies while actively exploring how to convert AI's vulnerability discovery capabilities into defensive advantages. AISI's ongoing evaluation work provides a valuable reference framework for industry and regulatory decision-making.
Key Takeaways
- The UK AI Safety Institute (AISI) completed its evaluation of OpenAI GPT-5.5's cybersecurity capabilities
- GPT-5.5's ability to discover security vulnerabilities is essentially on par with Anthropic's Claude Mythos
- The key difference is that GPT-5.5 is publicly available, while Claude Mythos was still in preview during its evaluation
- The public availability of AI models with advanced vulnerability discovery capabilities poses new challenges for security defense systems
- The convergence of top AI models in cybersecurity capabilities reflects shifting competitive dynamics in the industry
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