mlsanityData to 7 Oct 2026

Evaluating Coding Agents on Kernel Exploit Generation

arXiv 2609.25591 ↗First posted 22 Sep 2026cs.AI cs.CRcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Coding agents now find real vulnerabilities in production software. However, bug discovery results do not measure whether agents can construct exploit primitives. We introduce KEX-bench, a benchmark for evaluating coding agents on exploit primitive generation against real operating-system kernels. KEX-bench contains 45 task instances across 40 Linux and Windows CVEs, covering kernel address leak, instruction-pointer control, heap read, heap write, and arbitrary address write. Each task runs in an isolated virtual machine, exposes controlled tools, and uses a deterministic verifier to check primitive-specific success. We evaluate state-of-the-art coding agents paired with frontier and open-weight models under fixed tool-call budgets. Without a reference proof of concept (PoC), the strongest configuration solves 1 of 20 Windows tasks (5.0%) and 14 of 25 Linux tasks (56.0%). With a reference PoC, the strongest configuration solves 31 of 45 tasks (68.9%). This highlights the gap where agents reach kernel crashes but fail to shape kernel state into exploit primitives. We release KEX-bench for reproducible research on AI-assisted exploitation at https://kex-bench.github.io.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 36 references that are in the map. First posted on arXiv 22 Sep 2026.

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14 of 36 references.

Closest ancestors · 1

Same direction, newest first

  1. 2026DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks8 Jul

Other references · 13

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  2. 2023InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedbackcited by 229
  3. 2024LLM Agents can Autonomously Exploit One-day Vulnerabilitiescited by 125
  4. 2025SWE-Lancer: Can Frontier LLMs Earn 1 Million from Real-World Freelance Software Engineering?cited by 99
  5. 2024Teams of LLM Agents can Exploit Zero-Day Vulnerabilitiescited by 93
  6. 2024NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Securitycited by 90
  7. 2025CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilitiescited by 90
  8. 2025SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Taskscited by 69
  9. 2026ProgramBench: Can Language Models Rebuild Programs From Scratch?cited by 47
  10. 2026ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?cited by 24
  11. 2026ExploitBench: A Capability Ladder Benchmark for LLM Cybersecurity Agentscited by 16
  12. 2026SEC-bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?cited by 8
1 more
  1. 2019Automatic Techniques to Systematically Discover New Heap Exploitation Primitivescited by 2

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  4. 2026MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents
  5. 2026Patch-to-PoC: A Systematic Study of Agentic LLM Systems for Linux Kernel N-Day Reproduction
  6. 2026PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization
  7. 2026MOSAIC: Knowledge-Guided CLI Command Composition Attack in LLM Coding Agents
  8. 2026Don't Regenerate, Debug: A Domain-Specific Agent for Repairing Near-Miss Hardware Operators