SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks
SmoothLLM is a new algorithm that enhances the security of large language models against jailbreaking attacks by perturbing input prompts, showing significant robustness while being publicly available for further research.
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The paper titled "SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks" introduces a novel algorithm aimed at enhancing the security of large language models (LLMs) against jailbreaking attacks, which exploit vulnerabilities to generate inappropriate content. The authors, Alexander Robey, Eric Wong, Hamed Hassani, and George J. Pappas, propose SmoothLLM, which operates by perturbing input prompts at the character level and aggregating predictions to identify adversarial inputs. This method demonstrates significant robustness against various jailbreak techniques, including GCG, PAIR, RandomSearch, and AmpleGCG, and shows resilience against adaptive GCG attacks. While there is a minor trade-off between robustness and performance, SmoothLLM is compatible with any LLM and sets a new standard for defense mechanisms in this area. The code for the algorithm is publicly available, promoting further research and application in the field of machine learning and artificial intelligence.
- SmoothLLM is designed to defend LLMs against jailbreaking attacks.
- The algorithm uses character-level perturbations to enhance input security.
- It shows improved robustness against multiple jailbreak techniques.
- There is a small trade-off between robustness and nominal performance.
- The code for SmoothLLM is publicly accessible for further research.
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6 commentsJust skimmed the GitHub repo for this one and the read me mentions four additional llm inferences for each incoming request - so now we’ve 5x’ed the (already expensive) compute required to answer a query?
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