ExploitBench: A Capability Ladder Benchmark for LLM Cybersecurity Agents
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
- 2026ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?11 May
- 2026Patch-to-PoC: A Systematic Study of Agentic LLM Systems for Linux Kernel N-Day Reproduction7 Feb
- 2025SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks13 Jun
- 2025BountyBench: Dollar Impact of AI Agent Attackers and Defenders on Real-World Cybersecurity Systems21 May
- 2025CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities21 Mar
Other references · 3
Built on by
Citing papers per year: 2026: 16.
16 papers.
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By area:
- 2026The Last AI Built by Humans: Toward Genuine Recursive Self-Improvementcited by 5
- 2026AgentCyberRange: Benchmarking Frontier AI Systems in Realistic Cyber Rangescited by 4
- 2026The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmarkcited by 1
- 2026Beyond End-to-End Success: Diagnosing Failures in Long-Horizon Security LLM Agentscited by 1
- 2026SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agentscited by 1
- 2026Transferable Self-Evolving Playbooks for Agentic Security Auditingcited by 0
- 2026Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackerscited by 0
- 2026Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilitiescited by 0
- 2026StealthBench: Measuring Operational Stealth in Autonomous Offensive-Security Agentscited by 0
- 2026SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary systemcited by 0
- 2026PLCBench: Can Autonomous LLM Agents Turn PLC Access into Sustained Physical Impact?cited by 0
- 2026Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Modelscited by 0
4 more
- 2026MobileCybench: Evaluating Agent Vulnerability Discovery via Executable Probescited by 0
- 2026Evaluating Coding Agents on Kernel Exploit Generationcited by 0
- 2026AuraForge: Scaling Security Supervision for Training Coding Agentscited by 0
- 2026MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvementcited by 0
- 2026MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement8 Oct 2026
- 2026AuraForge: Scaling Security Supervision for Training Coding Agents1 Oct 2026
- 2026Evaluating Coding Agents on Kernel Exploit Generation22 Sep 2026
- 2026MobileCybench: Evaluating Agent Vulnerability Discovery via Executable Probes21 Sep 2026
- 2026The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement10 Sep 2026
- 2026Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models8 Sep 2026
- 2026SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents8 Sep 2026
- 2026PLCBench: Can Autonomous LLM Agents Turn PLC Access into Sustained Physical Impact?27 Aug 2026
- 2026Beyond End-to-End Success: Diagnosing Failures in Long-Horizon Security LLM Agents20 Aug 2026
- 2026SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system15 Aug 2026
- 2026The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark11 Aug 2026
- 2026StealthBench: Measuring Operational Stealth in Autonomous Offensive-Security Agents28 Jul 2026
4 more
- 2026Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilities25 Jun 2026
- 2026Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackers19 Jun 2026
- 2026Transferable Self-Evolving Playbooks for Agentic Security Auditing15 Jun 2026
- 2026AgentCyberRange: Benchmarking Frontier AI Systems in Realistic Cyber Ranges12 Jun 2026
- 2026The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmarkcited by 1
- 2026Beyond End-to-End Success: Diagnosing Failures in Long-Horizon Security LLM Agentscited by 1
- 2026SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agentscited by 1
- 2026Transferable Self-Evolving Playbooks for Agentic Security Auditingcited by 0
- 2026Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilitiescited by 0
- 2026StealthBench: Measuring Operational Stealth in Autonomous Offensive-Security Agentscited by 0
- 2026MobileCybench: Evaluating Agent Vulnerability Discovery via Executable Probescited by 0
- 2026Evaluating Coding Agents on Kernel Exploit Generationcited by 0
- 2026AuraForge: Scaling Security Supervision for Training Coding Agentscited by 0
- 2026AuraForge: Scaling Security Supervision for Training Coding Agents1 Oct 2026
- 2026Evaluating Coding Agents on Kernel Exploit Generation22 Sep 2026
- 2026MobileCybench: Evaluating Agent Vulnerability Discovery via Executable Probes21 Sep 2026
- 2026SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents8 Sep 2026
- 2026Beyond End-to-End Success: Diagnosing Failures in Long-Horizon Security LLM Agents20 Aug 2026
- 2026The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark11 Aug 2026
- 2026StealthBench: Measuring Operational Stealth in Autonomous Offensive-Security Agents28 Jul 2026
- 2026Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilities25 Jun 2026
- 2026Transferable Self-Evolving Playbooks for Agentic Security Auditing15 Jun 2026
- 2026AgentCyberRange: Benchmarking Frontier AI Systems in Realistic Cyber Rangescited by 4
- 2026SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary systemcited by 0
- 2026PLCBench: Can Autonomous LLM Agents Turn PLC Access into Sustained Physical Impact?cited by 0
- 2026Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Modelscited by 0
- 2026MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvementcited by 0
- 2026MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement8 Oct 2026
- 2026Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models8 Sep 2026
- 2026PLCBench: Can Autonomous LLM Agents Turn PLC Access into Sustained Physical Impact?27 Aug 2026
- 2026SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system15 Aug 2026
- 2026AgentCyberRange: Benchmarking Frontier AI Systems in Realistic Cyber Ranges12 Jun 2026
Similar, not cited
Close in wording, no citation either way
- 2026ChainFuzzer: Greybox Fuzzing for Workflow-Level Multi-Tool Vulnerabilities in LLM Agents
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- 2024Catastrophic Cyber Capabilities Benchmark (3CB): Robustly Evaluating LLM Agent Cyber Offense Capabilities
- 2026SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization
- 2026ZeroDayBench: Evaluating LLM Agents on Unseen Zero-Day Vulnerabilities for Cyberdefense
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- 2026SecProbe: Adaptive Evaluation of Coding Agents on Cybersecurity Vulnerabilities
- 2025SoK: Measuring What Matters for Closed-Loop Security Agents