XBOW's early access testing shows GPT-5.5 matches Anthropic's restricted Mythos model on penetration testing benchmarks — and OpenAI is releasing it publicly.
XBOW, an AI-powered offensive security firm, conducted early access testing of OpenAI's GPT-5.5 inside their agent-based penetration testing workflows. The model delivered a measurable step-change in vulnerability detection and exploitation tasks — performance they describe as comparable to Anthropic's Mythos, which remains restricted. Unlike Mythos, GPT-5.5 is being released broadly. XBOW reports GPT-5.5 is now leading across several of their internal benchmarks, describing it as 'generally more powerful' than prior model generations.
GPT-5.5 isn't just incrementally better — XBOW's agent-in-the-loop benchmarks show it outperforming previous frontier models on multi-step offensive security tasks including login flows, vuln discovery, and report generation. This isn't a chat benchmark; it's performance inside a real agentic stack. For developers building security tooling or agent pipelines, this is a meaningful capability jump available via the standard OpenAI API.
If you're running any automated security scanning or agent workflow on OpenAI's API, swap your current model config to GPT-5.5 and run your existing test suite — XBOW's results suggest you'll see measurable accuracy gains on multi-step reasoning tasks without re-architecting anything.
Suggested exercise · not independently tested. Access requirements and costs may vary.
Open your terminal and ensure your OPENAI_API_KEY is set
Run: curl https://api.openai.com/v1/chat/completions -H 'Authorization: Bearer $OPENAI_API_KEY' -H 'Content-Type: application/json' -d '{"model": "gpt-5.5", "messages": [{"role": "user", "content": "You are a penetration tester. Identify potential SQL injection entry points in a login form that accepts username and password as POST parameters. List 5 payloads to test."}]}'
Compare the specificity and accuracy of payloads returned versus your current model's output on the same prompt
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