mlsanityData to 7 Oct 2026

ExploitBench: A Capability Ladder Benchmark for LLM Cybersecurity Agents

arXiv 2605.14153 ↗First posted 13 May 2026cs.CR cs.AIcited by 16

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Exploitation is not a binary event. It is a ladder of acquiring progressive capabilities, from executing a single buggy line of code to taking full control of the target. However, existing LLM security benchmarks treat a crash as exploitation success. That single binary outcome collapses the hard parts of exploitation: the transition from triggering a bug to constructing reusable primitives and control. We present ExploitBench, a capability-graded benchmark that decomposes exploitation into 16 measurable flags, from coverage and crash through sandbox primitives, arbitrary read/write, control-flow hijack, and arbitrary code execution. Each capability is verified by a deterministic oracle that uses a per-run randomized challenge-response for primitives, differential execution against ground-truth binaries to measure progress, and a signal-handler proof for code execution. We instantiate ExploitBench on 41 V8 bugs because V8 is both widely deployed and exploitation-hardened. We report three arms: <model,env> as the primary measurement of model-environment capability, <model,env, adaptive coaching> as a secondary arm that adds adaptive coaching to test whether targeted feedback shifts outcomes, and <model,env,harness> as an ablation that swaps in the model's native CLI to check whether vendor-side optimizations increase exploitation capabilities. Our results show a sharp capability split between publicly deployed frontier models and the private frontier. Across the 8 publicly deployed models tested, reaching the vulnerable code and triggering a crash is routine, but arbitrary code execution is not. The private model shows arbitrary code execution on approximately half. Overall, results suggest that exploit construction against hardened targets is an emerging frontier capability.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 8 of its 27 references that are in the map. First posted on arXiv 13 May 2026.

Builds on

8 of 27 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?11 May
  2. 2026Patch-to-PoC: A Systematic Study of Agentic LLM Systems for Linux Kernel N-Day Reproduction7 Feb
  3. 2025SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks13 Jun
  4. 2025BountyBench: Dollar Impact of AI Agent Attackers and Defenders on Real-World Cybersecurity Systems21 May
  5. 2025CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities21 Mar

Other references · 3

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  2. 2024CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligencecited by 61
  3. 2026LLM-Based Agents for Identifying Bug-Introducing Commitscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 16.

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  1. 2026ChainFuzzer: Greybox Fuzzing for Workflow-Level Multi-Tool Vulnerabilities in LLM Agents
  2. 2026Systematic Capability Benchmarking of Frontier Large Language Models for Offensive Cyber Tasks
  3. 2024Catastrophic Cyber Capabilities Benchmark (3CB): Robustly Evaluating LLM Agent Cyber Offense Capabilities
  4. 2026SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization
  5. 2026ZeroDayBench: Evaluating LLM Agents on Unseen Zero-Day Vulnerabilities for Cyberdefense
  6. 2026MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents
  7. 2026SecProbe: Adaptive Evaluation of Coding Agents on Cybersecurity Vulnerabilities
  8. 2025SoK: Measuring What Matters for Closed-Loop Security Agents