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Automatic Exploit Generation with LLMs
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# Anamnesis: LLM Exploit Generation Evaluation This repository contains the evaluation framework for studying how LLM agents generate exploits from vulnerability reports in the presence of exploit mitigations. Given a bug report and proof-of-concept trigger, agents analyze vulnerable software and produce working exploits that bypass various security mitigations. In the experiments I used a zeroday vulnerability in QuickJS as the starting point, and then asked agents built on top of Opus 4.5 and GPT-5.2 to generate exploits. Across the experiments I varied the protection mechanisms enabled and the requirements of the exploits. Opus 4.5 solved many of the tasks, and GPT-5.2 solved all of them. Both models produced exploits that used the vulnerability to build an 'API' to allow them to modify the target processes address space at will. They then used that mechanism to defeat protection mechanisms, hijack execution and achieve their objectives. The QuickJS vulnerability is explained in detail below. It was also automatically discovered (using an agent I built on top of Opus 4.5). This document focuses on the experiments and the technical aspects of the exploits. I've written up my broader thoughts on the topic and what conclusions I've drawn from the experiments on my [blog](https://sean.heelan.io/2026/01/18/on-the-coming-industrialisation-of-exploit-generation-with-llms/). **To run your own experiments, see [QUICKSTART.md](QUICKSTART.md).** ## Table of Contents - [Experiments and Results](#experiments-and-results) - [Notable Exploits](#notable-exploits) - [Agent Anatomy](#agent-anatomy) - [Understanding the Protections and Their Gaps](#understanding-the-protections-and-their-gaps) - [The Vulnerability](#the-vulnerability) - [Partial RELRO: Building Exploit Primitives](#partial-relro-building-exploit-primitives) - [The Hardest Challenge: RELRO, CFI, ShadowStack and a Sandbox](#the-hardest-challenge-relro-cfi-shadowstack-and-a-sandbox) - [Exploit Enhancement Experiments](#exploit-enhancement-experiments) ## Experiments and Results I evaluated two frontier models: **Claude Opus 4.5** and **GPT-5.2**. I gave both the same vulnerability (a use-after-free in QuickJS) and challenged them to produce working exploits across increasingly difficult mitigation configurations. I gave the models a budget of 30M tokens per run, with no hints about how to bypass specific protections. Unless otherwise mentioned, I ran 10 agents per model for each experiment. I used Opus 4.5 via the Claude Agent SDK and GPT-5.2 via the OpenAI Agents SDK. I set Opus's thinking budget to its highest: 31999, and GPT-5.2's reasoning setting to 'high'. The one exception to these settings was the `Full RELRO + CFI + Shadow Stack + Sandbox` experiment. To concentrate resources, on this experiment I only ran GPT-5.2. I set its token budget to 60M and its reasoning setting to 'xhigh'. I selected GPT-5.2 over Opus 4.5 for this task as it had performed better on harder tasks than Opus and it seemed more likely to succeed. See`run_experiments.py` for how to run the experiments. The full record of the experiments I ran, including the agent work log and the exploits are in the `experiment-results` directory. One note worth making is that 10 runs per experiment is too low to make definitive statements about the relative capabilities of the models. It does seem that GPT-5.2 has an edge, in that it tended to be faster, more efficient, solve more tasks and solve harder tasks. To make a definitive statement either way you would need to do more runs. See the [Understanding the Protections and Their Gaps](#understanding-the-protections-and-their-gaps) section later for a full explanation of the mitigations, their known flaws, and what each scenario involves. *Note: In every scenario Address Space Layout Randomisation (ASLR) and non-executable memory (NX, also called DEP) were enabled.* ### Partial RELRO The baseline configuration with ASLR, NX, PIE, and a writabl
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matched fp:a54cfaa20554db49, llm:Repository description: "Automatic Exploit Generation with LLMs" (name: anamnesis-release). Language: JavaScript. No topics provided.
matched fp:a54cfaa20554db49, llm:Repository description: "Automatic Exploit Generation with LLMs" (name: anamnesis-release). Language: JavaScript. No topics provided.
matched fp:a54cfaa20554db49, llm:Repository description: "Automatic Exploit Generation with LLMs" (name: anamnesis-release). Language: JavaScript. No topics provided.