ReproBench: Benchmarking LLM Agents on Reproducing Vulnerability From Scratch
cs.CR
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/ReFirmLabs/binwalk
Terminology
Sources
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
- ExploitBench: A Capability Ladder Benchmark for LLM Cybersecurity Agents
- A Dual-Loop Agent Framework for Automated Vulnerability Reproduction
- Patch-to-PoC: A Systematic Study of Agentic LLM Systems for Linux Kernel N-Day Reproduction
- CyberGym-E2E: Scalable Real-World Benchmark for AI Agents' End-to-End Cybersecurity Capabilities
- ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?
- CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale
- ProgramBench: Can Language Models Rebuild Programs From Scratch?
- GLM-5: from Vibe Coding to Agentic Engineering
- A Systematic Study on Generating Web Vulnerability Proof-of-Concepts Using Large Language Models
- CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities
Related papers
- SoK: AI-Augmented Binary Reversing
- Relaxed Sender Anonymity for CBDC Interbank Settlement: A Zero-Knowledge Approach on Permissioned EVM
- Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages
- Efficient Fuzzy PSI under One-Sided Assumptions
- Sealing the Audit-Runtime Gap for LLM Skills
- Token Composition: A Graph Based on EVM Logs