Activation-Guided Local Editing for Jailbreaking Attacks
Summary: arXiv:2508.00555v2 Announce Type: replace-cross
Abstract: Jailbreaking is an essential adversarial technique for red-teaming these models to uncover and patch security flaws. However, existing jailbreak methods face significant drawbacks. Token-level jailbreak attacks often produce incoherent or unreadable inputs and exhibit poor transferability, while prompt-level attacks lack scalability and rely heavily on manual effort and human ingenuity. We propose a concise and effective two-stage framework that combines the advantages of these approaches.
The Two-Stage Framework
The first stage of our proposed framework performs a scenario-based generation of context and rephrases the original malicious query to obscure its harmful intent. This enables the model to process the input without recognizing its malicious nature. The second stage utilizes information from the model’s hidden states to guide fine-grained edits, effectively steering the model’s internal representation of the input from a malicious toward a benign one.
Key Advantages
- Improved Attack Success Rate: Extensive experiments demonstrate that our method achieves state-of-the-art Attack Success Rate, with gains of up to 37.74% over the strongest baseline.
- Excellent Transferability: The proposed method exhibits excellent transferability to black-box models, making it more versatile in real-world applications.
- Resistance to Defense Mechanisms: Our analysis further demonstrates that AGILE maintains substantial effectiveness against prominent defense mechanisms, highlighting the limitations of current safeguards.
Implications for Future Defense Development
The findings from our research provide valuable insights for future defense development. The limitations identified within current defenses suggest that further innovations are necessary to keep pace with evolving adversarial techniques. By understanding how AGILE operates and its effectiveness against existing safeguards, researchers and developers can create more robust solutions to counteract jailbreak attacks.
Conclusion
In summary, the development of the Activation-Guided Local Editing (AGILE) framework marks a significant advancement in the field of adversarial machine learning. By addressing the shortcomings of traditional jailbreak methods, AGILE offers a promising avenue for improving the security of machine learning models. Our code is available for those interested in exploring this innovative approach further at https://github.com/SELGroup/AGILE.