Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking
arXiv:2602.24009v1 Announce Type: cross Abstract: Jailbreak techniques for large language models (LLMs) evolve faster than benchmarks, making robustness estimates stale and difficult to compare across papers due to drift in datasets, harnesses, and judging protocols. We introduce JAILBREAK FOUNDRY (JBF), a system that addresses this gap via a multi-agent workflow to translate jailbreak papers into executable modules for immediate evaluation within a unified harness. JBF features three core components: (i) JBF-LIB for shared contracts and reusable utilities; (ii) JBF-FORGE for the multi-agent paper-to-module translation; and (iii) JBF-EVAL for standardizing evaluations. Across 30 reproduced attacks, JBF achieves high fidelity with a mean (reproduced-reported) attack success rate (ASR) deviation of +0.26 percentage points. By leveraging shared infrastructure, JBF reduces attack-specific implementation code by nearly half relative to original repositories and achieves an 82.5% mean reuse
arXiv:2602.24009v1 Announce Type: cross Abstract: Jailbreak techniques for large language models (LLMs) evolve faster than benchmarks, making robustness estimates stale and difficult to compare across papers due to drift in datasets, harnesses, and judging protocols. We introduce JAILBREAK FOUNDRY (JBF), a system that addresses this gap via a multi-agent workflow to translate jailbreak papers into executable modules for immediate evaluation within a unified harness. JBF features three core components: (i) JBF-LIB for shared contracts and reusable utilities; (ii) JBF-FORGE for the multi-agent paper-to-module translation; and (iii) JBF-EVAL for standardizing evaluations. Across 30 reproduced attacks, JBF achieves high fidelity with a mean (reproduced-reported) attack success rate (ASR) deviation of +0.26 percentage points. By leveraging shared infrastructure, JBF reduces attack-specific implementation code by nearly half relative to original repositories and achieves an 82.5% mean reused-code ratio. This system enables a standardized AdvBench evaluation of all 30 attacks across 10 victim models using a consistent GPT-4o judge. By automating both attack integration and standardized evaluation, JBF offers a scalable solution for creating living benchmarks that keep pace with the rapidly shifting security landscape.
Executive Summary
The article introduces JAILBREAK FOUNDRY (JBF), a system that bridges the gap between jailbreak techniques for large language models (LLMs) and reproducible benchmarking. JBF translates jailbreak papers into executable modules for immediate evaluation within a unified harness, featuring three core components: JBF-LIB, JBF-FORGE, and JBF-EVAL. The system achieves high fidelity across 30 reproduced attacks, reducing attack-specific implementation code by nearly half and promoting a standardized AdvBench evaluation. JBF's automation of attack integration and evaluation enables scalable creation of living benchmarks that keep pace with the rapidly shifting security landscape.
Key Points
- ▸ JBF translates jailbreak papers into executable modules for immediate evaluation
- ▸ The system achieves high fidelity across 30 reproduced attacks
- ▸ JBF reduces attack-specific implementation code by nearly half
Merits
Strength in Reproducibility
JBF's ability to translate papers into executable modules and achieve high fidelity across reproduced attacks demonstrates its effectiveness in promoting reproducible benchmarking.
Efficiency in Code Reuse
JBF's shared infrastructure reduces attack-specific implementation code by nearly half, promoting code reuse and efficiency in benchmarking.
Demerits
Dependence on Unified Harness
JBF's performance may be contingent on the unified harness, which may not be universally applicable or adaptable to different LLMs.
Potential for Overreliance on Automation
JBF's automation of attack integration and evaluation may lead to overreliance on the system, potentially masking underlying vulnerabilities or issues in the LLMs being tested.
Expert Commentary
JBF's introduction marks a significant advancement in the field of adversarial attacks on LLMs. By bridging the gap between jailbreak techniques and reproducible benchmarking, JBF enables researchers and developers to create living benchmarks that keep pace with the rapidly shifting security landscape. The system's high fidelity across reproduced attacks and efficient code reuse demonstrate its effectiveness in promoting reproducible benchmarking. However, JBF's dependence on a unified harness and potential for overreliance on automation remain concerns that must be addressed. Ultimately, JBF's implications for model robustness and security, as well as its potential impact on policy and regulation, highlight the need for continued research and development in this critical area.
Recommendations
- ✓ Researchers and developers should leverage JBF to create living benchmarks that promote reproducible benchmarking and improve the robustness and security of LLMs.
- ✓ Policymakers and regulatory bodies should consider the implications of JBF for model robustness and security, and reevaluate their approaches to regulating AI and machine learning technologies.