Antiproof: Neuro-Symbolic System Discovers Hundreds of Zero-Days, Including RCE in LLM Infrastructure

Jul 15, 2026

Antiproof is a vulnerability discovery system that combines neuro-symbolic detector synthesis with proof-of-exploitability oracles to achieve high-recall vulnerability detection and automatic validation. In evaluations, it detected 64 of 66 vulnerabilities in benchmarks, improving recall by over 60 percentage points compared to baselines, and uncovered several hundred previously unknown vulnerabilities in widely deployed systems. The system has received 12 CVE assignments, including remote code execution vulnerabilities in Ray, SGLang, vLLM, and LiteLLM, which could allow attackers to compromise LLM training and inference systems.

Why it matters: This work demonstrates a scalable and effective approach to discovering and validating zero-day vulnerabilities in critical AI infrastructure, with immediate security implications for widely used LLM deployment tools.

Full story at: arXiv Cryptography and Security ↗