GPT-5.5 Matches Claude Mythos in Security Vulnerability Discovery: UK AI Security Institute Report

The ability of large language models to identify security vulnerabilities is rapidly advancing, with recent evaluations suggesting that OpenAI's GPT-5.5 performs at a level comparable to Anthropic's Claude Mythos in this critical domain. The UK's AI Security Institute has conducted an assessment revealing that these two models are essentially neck-and-neck when it comes to finding weaknesses in code and systems. Not only does GPT-5.5 achieve parity with Mythos, but it also comes with the advantage of being generally available to the public, whereas Mythos has had more limited release. Additionally, the Institute has analyzed a smaller, more cost-effective model that, despite requiring more careful prompting and scaffolding, yields results that are just as effective. This article delves into the findings and what they mean for cybersecurity and AI development.

The Growing Role of AI in Cybersecurity

Cybersecurity professionals are increasingly turning to artificial intelligence to help automate the detection of vulnerabilities in software and networks. Large language models (LLMs) like GPT-5.5 and Claude Mythos have been trained on vast amounts of code and security data, enabling them to identify patterns indicative of flaws such as SQL injection, buffer overflows, and improper authentication. The UK AI Security Institute (formerly known as the AI Safety Institute) was established to rigorously test these models and provide independent assessments of their capabilities. Their latest evaluation sheds light on how these frontier models stack up against each other in a security context.

GPT-5.5 Matches Claude Mythos in Security Vulnerability Discovery: UK AI Security Institute Report — Source: www.schneier.com

The UK AI Security Institute's Evaluation

The Institute's assessment focused on the ability of GPT-5.5 and Claude Mythos to locate and describe security vulnerabilities in a controlled test environment. The evaluation involved presenting both models with a set of code samples and system configurations known to contain flaws, then measuring their accuracy and completeness in identifying them. The results showed that GPT-5.5 performed at a level statistically indistinguishable from Claude Mythos. This is significant because Mythos, released earlier, had been considered a benchmark for LLM-based vulnerability detection. The fact that a generally available model like GPT-5.5 can match it suggests that state-of-the-art vulnerability detection is becoming more accessible to developers and security teams.

General Availability of GPT-5.5

One key differentiator is that GPT-5.5 is available through OpenAI's API and consumer offerings, while Claude Mythos has been more restricted. This means that organizations can integrate GPT-5.5 into their security toolchains without the need for special access or partnerships. The Institute's report highlights that this broader availability could democratize access to advanced vulnerability scanning, especially for smaller firms that may not have the resources to deploy dedicated security tools.

Analysis of a Smaller, Cheaper Model

Beyond the flagship comparison, the Institute also examined a smaller, more cost-effective model (details of which are available in their separate analysis). This model, while less powerful out-of-the-box, proved surprisingly capable when given additional guidance. The key finding is that with more careful scaffolding—that is, explicit instructions, context, and iterative prompting from the user—this smaller model achieved vulnerability detection results on par with its larger counterparts. This indicates that even teams with limited budgets can achieve high-quality security analysis by investing in prompt engineering and human-in-the-loop processes.

The Role of Scaffolding

The term "scaffolding" refers to the structured prompts, few-shot examples, and contextual cues that guide an LLM toward the desired output. For the smaller model, the researchers found that a well-designed scaffolding approach significantly reduced false positives and improved coverage. This finding is encouraging because it suggests that the barrier to entry for using LLMs in vulnerability detection is not insurmountable; with the right techniques, even a less expensive model can be a valuable asset.

Implications for Security Practitioners

These results have several implications. First, the near-equivalence of GPT-5.5 and Claude Mythos suggests that organizations can choose between them based on factors like cost, integration ease, or compliance requirements, without sacrificing detection performance. Second, the success of the smaller model with scaffolding indicates that budget-constrained teams should not dismiss lightweight LLMs; instead, they should invest in developing robust prompt strategies. Finally, the Institute's work underscores the importance of independent evaluation to help the community understand the real-world capabilities of AI models in security tasks.

Choice flexibility: Multiple models now offer top-tier vulnerability detection.
Cost-effective options: Smaller models can be highly effective with proper scaffolding.
Need for human oversight: Prompt engineering remains a critical skill.

Conclusion

The UK AI Security Institute's evaluation confirms that GPT-5.5 is as good as Claude Mythos at finding security vulnerabilities, offering a generally available alternative. Moreover, the analysis of a smaller, cheaper model shows that with careful scaffolding, cost is not an insurmountable barrier to quality vulnerability detection. These findings provide a roadmap for integrating LLMs into cybersecurity workflows, whether through frontier models or more accessible options. As the field evolves, continued evaluation and transparency will be essential to ensure that AI tools are deployed safely and effectively.

For more details, see the Institute's full evaluation of Claude Mythos and the analysis of the smaller model.