# Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilities

arXiv: https://arxiv.org/abs/2606.26933 · HTML: https://mlsanity.com/paper/2606.26933/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 65 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 12 of 12)

- [LLM Agents can Autonomously Exploit One-day Vulnerabilities](https://mlsanity.com/paper/2404.08144/) (2024)
- [CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale](https://mlsanity.com/paper/2506.02548/) (2025)
- [Small World with High Risks: A Study of Security Threats in the npm Ecosystem](https://mlsanity.com/paper/1902.09217/) (2019)
- [ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?](https://mlsanity.com/paper/2605.11086/) (2026)
- [Prompt Fuzzing for Fuzz Driver Generation](https://mlsanity.com/paper/2312.17677/) (2023)
- [ExploitBench: A Capability Ladder Benchmark for LLM Cybersecurity Agents](https://mlsanity.com/paper/2605.14153/) (2026)
- [Incalmo: An Autonomous LLM-assisted System for Red Teaming Multi-Host Networks](https://mlsanity.com/paper/2501.16466/) (2025)
- [ELFuzz: Efficient Input Generation via LLM-driven Synthesis Over Fuzzer Space](https://mlsanity.com/paper/2506.10323/) (2025)
- [SoK: DARPA's AI Cyber Challenge (AIxCC): Competition Design, Architectures, and Lessons Learned](https://mlsanity.com/paper/2602.07666/) (2026)
- [Generating API Parameter Security Rules with LLM for API Misuse Detection](https://mlsanity.com/paper/2409.09288/) (2024)
- [FuzzingBrain V2: A Multi-Agent LLM System for Automated Vulnerability Discovery and Reproduction](https://mlsanity.com/paper/2605.21779/) (2026)
- [OSS-CRS: Liberating AIxCC Cyber Reasoning Systems for Real-World Open-Source Security](https://mlsanity.com/paper/2603.08566/) (2026)

## Abstract (arXiv)

AI-assisted vulnerability discovery has proven effective for bug classes like memory safety, where instrumentation confirms memory violations and efficiently filters false positives. Many dangerous vulnerability classes, such as cryptographic misuse, however, lack any comparable instrumentation. In this work, we present Chai, an AI-based system that discovers and validates cryptographic misuse vulnerabilities through naturally occurring signals. To achieve this, Chai rethinks the classical technique of differential testing by leveraging AI to 1) improve precision for detecting real security issues in libraries, and 2) repurpose commonly overlooked discrepancies as leads for tangible vulnerabilities in downstream applications. In doing so, Chai inverts the prevailing paradigm of AI vulnerability discovery: instead of auditing one codebase for many flaws, it catalogs flaws at the library level and propagates them across a cryptographic dependency graph, delivering compounding efficiency gains. We evaluate Chai across X.509, JWT, and SAML libraries. Chai discovered a previously unknown critical vulnerability in an SSL library that powers billions of devices, along with security bugs in one library behind a major web browser and another in major Linux distributions. In total, these techniques surfaced over 100 vulnerabilities.