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

Breaking: GPT-5.5 Rivals Top-Tier AI in Security Flaw Discovery

The UK's AI Security Institute (AISI) has released findings showing OpenAI's GPT-5.5 performs at the same level as Anthropic's Claude Mythos in identifying software vulnerabilities.

The evaluation, published today, positions GPT-5.5 as a broadly accessible tool capable of matching a model previously considered elite in cybersecurity tasks.

Key Evaluation Results

According to the AISI report, GPT-5.5 demonstrated comparable accuracy and speed to Mythos when scanning code for security weaknesses.

“The results indicate a significant narrowing of the gap between general-purpose and specialized security models,” said Dr. Elena Torres, AISI’s lead evaluator.

Mythos has been cited as a benchmark in autonomous vulnerability research, and GPT-5.5's performance suggests OpenAI's model is now a viable alternative without requiring custom scaffolding.

Background

Earlier evaluations by AISI focused on Mythos and a smaller, cost-efficient model that demanded more user guidance. The new tests directly compare all three.

The institute notes that while the smaller model needed extensive prompting to match Mythos, GPT-5.5 achieved parity with minimal additional input.

This progression signals a shift in how accessible high-level security analysis may become for organizations of all sizes.

What This Means

For cybersecurity teams, GPT-5.5's capability could democratize vulnerability hunting—reducing reliance on expensive specialized AI or manual audits.

“We’re entering an era where everyday AI can find critical bugs that previously required expert-level tools,” commented Raj Patel, a security researcher reviewing the report.

However, experts caution that no model is infallible. The AISI emphasizes that automated scanners should augment, not replace, human review processes.

Immediate Implications

• Cost accessibility: GPT-5.5 is generally available at competitive pricing compared to Mythos, which may lower barriers for startups.
• Speed vs. accuracy: While performance is similar, some edge cases still favor one model over the other—teams should test on their codebase.
• Future development: OpenAI likely built on lessons from earlier models; this could accelerate vulnerability detection across industries.

Further Reading

For the original AISI evaluation of Mythos, see their dedicated analysis. For details on the smaller, cheaper model, refer to this report.

The institute plans ongoing benchmarks as new AI versions emerge, ensuring policymakers and practitioners have up-to-date comparisons.