GPT-5.5 Matches Mythos in Security Vulnerability Detection, UK AI Security Institute Reveals

Breaking: GPT-5.5 Achieves Top-Tier Security Vulnerability Detection

LONDON — The UK’s AI Security Institute (UK AISI) has released findings showing that OpenAI’s GPT-5.5 model is now on par with Anthropic’s Mythos in identifying software security vulnerabilities. The assessment, conducted under controlled conditions, marks a rapid escalation in the offensive capabilities of widely available AI systems.

“GPT-5.5 demonstrates a statistically indistinguishable performance from Mythos when tasked with finding critical flaws in codebases,” said Dr. James Whitfield, Acting Director of UK AISI. “This is concerning because GPT-5.5 is a general‑purpose model available to anyone, whereas Mythos is subject to stricter usage controls.”

Background

The evaluation tested GPT-5.5 against a curated set of real‑world vulnerabilities across multiple languages. Mythos, developed by Anthropic, has long been considered the benchmark for AI‑assisted security research. Previous comparisons focused on smaller, open‑source models that required extensive human guidance.

According to the Institute’s report, GPT-5.5 required no more than standard prompt engineering to match Mythos’s success rate. A second experiment using a smaller, cheaper model — identified only as Model X — also matched both models but demanded significantly more scaffolding from the prompter.

“That a budget model can equal the big players with proper guidance suggests the barrier to entry for automated vulnerability hunting is already very low,” noted Dr. Lisa Cheng, a cybersecurity researcher at Oxford University who reviewed the findings.

Evaluation Details

• Models tested: GPT-5.5 (OpenAI), Mythos (Anthropic), Model X (undisclosed smaller model)
• Dataset: 500 known vulnerabilities from CVE records, ranging from buffer overflows to SQL injection
• Metric: Success rate at identifying the vulnerable function and proposing a secure fix
• Result: GPT-5.5 and Mythos achieved 94% and 95% success rates respectively, statistically equivalent

What This Means

The findings have immediate implications for software supply chain security. If a widely deployed model like GPT-5.5 can find vulnerabilities as effectively as purpose‑built security tools, attackers can scale up their reconnaissance at minimal cost. Defenders must now assume that all code reviewed by an AI could be analyzed just as effectively by an adversary’s AI.

“The democratization of vulnerability discovery is a double‑edged sword,” said Whitfield. “Organizations should treat any code that passes through a public AI chat interface as potentially exposed.” The Institute recommends that companies adopt more rigorous code‑scanning pipelines and consider using the same models proactively to patch issues before they are exploited.

However, the smaller model’s performance underscores another concern: even resource‑constrained actors can achieve high accuracy with enough human effort. “We’re entering an era where skill, not compute, is the limiting factor,” added Dr. Cheng.

The UK AISI plans to release the full benchmarking methodology next month. In the meantime, it has advised developers to update their threat models to account for the widespread availability of GPT-5.5.

This is a developing story. More details will follow as the Institute publishes its complete dataset.