Spencer Mcintyre

AI Summary

→ WHAT IT COVERS Spencer Mc, Director of Exploit Development at VulnerCheck, covers how AI is reshaping vulnerability research and exploit development, the distinction between vulnerabilities and exploits, why over 50% of public proof-of-concept exploits on GitHub are now non-functional, and where agentic AI fits into offensive security workflows. → KEY INSIGHTS - **Vulnerability vs. Exploit distinction:** A vulnerability is the theoretical flaw; an exploit is the concrete implementation that proves attacker utility. Most AI models identify vulnerabilities well but consistently fail to bridge the gap to functional exploitation, meaning CVSS scores alone cannot drive patch prioritization without understanding real-world attacker utility for your specific environment. - **POC quality tipping point:** VulnerCheck's analysis of public proof-of-concept exploits across GitHub, GitLab, and CodeBerg found that in 2026, non-functional or low-quality POCs crossed 50% of all published submissions. Security teams should validate any public POC against a live target before treating it as confirmed attacker capability or using it to justify emergency remediation timelines. - **AI exploit capability by vulnerability class:** LLMs reliably produce functional exploits for open-source web application flaws — logic errors, token handling bugs, expression language injections, file leaks — but struggle significantly with closed-source firmware and proprietary x86 software requiring patch diffing or reverse engineering. Teams should allocate human researchers specifically to closed-source targets rather than relying on AI-generated output. - **Go Exploit framework as LLM scaffold:** VulnerCheck uses its open-source Go Exploit framework as a structured corpus when prompting LLMs for exploit development. Having a normalized, growing library of 50-plus new exploits per week reduces LLM hallucination and scaffolding errors. Security teams building AI-assisted research pipelines should establish a structured framework before prompting models, not after. - **Guardrail workaround via local models:** Proprietary American AI models have progressively tightened restrictions on offensive security tasks, with researchers reporting meaningful capability drops after Anthropic's Claude Opus 4.6/4.8. Anthropic's own benchmarks show GLM 5.3 running locally at roughly 90% of frontier model performance. Offensive security teams handling sensitive vulnerability data are migrating to self-hosted open-weight models to avoid both guardrails and data exposure risks. → NOTABLE MOMENT Spencer Mc described a case where someone used Claude autonomously to exploit a SQL injection vulnerability — not for malicious gain, but simply to jump a spin class waitlist. He flagged this as a signal that legal precedent around AI-assisted unauthorized access remains entirely unresolved. 💼 SPONSORS [{"name": "Hostinger", "url": "https://hostinger.com/practical"}] 🏷️ Exploit Development, Vulnerability Research, AI Cybersecurity, Agentic AI, Offensive Security