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RECAP: A Resource-Efficient Method for Adversarial Prompting in Large Language Models

Authors: Rishit Chugh

Published: 2026-01-20

arXiv ID: 2601.15331v1

Added to Library: 2026-01-23 03:01 UTC

Red Teaming

📄 Abstract

The deployment of large language models (LLMs) has raised security concerns due to their susceptibility to producing harmful or policy-violating outputs when exposed to adversarial prompts. While alignment and guardrails mitigate common misuse, they remain vulnerable to automated jailbreaking methods such as GCG, PEZ, and GBDA, which generate adversarial suffixes via training and gradient-based search. Although effective, these methods particularly GCG are computationally expensive, limiting their practicality for organisations with constrained resources. This paper introduces a resource-efficient adversarial prompting approach that eliminates the need for retraining by matching new prompts to a database of pre-trained adversarial prompts. A dataset of 1,000 prompts was classified into seven harm-related categories, and GCG, PEZ, and GBDA were evaluated on a Llama 3 8B model to identify the most effective attack method per category. Results reveal a correlation between prompt type and algorithm effectiveness. By retrieving semantically similar successful adversarial prompts, the proposed method achieves competitive attack success rates with significantly reduced computational cost. This work provides a practical framework for scalable red-teaming and security evaluation of aligned LLMs, including in settings where model internals are inaccessible.

🔍 Key Points

  • Introduction of RECAP, a resource-efficient adversarial prompting method that retrieves pre-trained adversarial prompts instead of generating them, saving time and resources.
  • Categorization of a dataset of 1,000 prompts into seven harm-related categories to facilitate better target matching for adversarial prompting.
  • RECAP leverages a retrieval database and hierarchical success rates of adversarial techniques (GCG, PEZ, GBDA) to enhance the effectiveness of attacks against large language models.
  • Demonstration of competitive attack success rates with RECAP, achieving 33% success while being considerably faster (4 minutes) than methods like GCG which took approximately 8 hours for similar tasks.
  • Applicability of RECAP to black-box models, which enhances its utility in real-world scenarios where model internals are not accessible.

💡 Why This Paper Matters

This paper is significant as it presents a novel method to improve the evaluation of security in large language models without the extensive computational resources typically required for adversarial prompting. By utilizing a retrieval-based approach, it remains accessible for smaller organizations, allowing for more effective adversarial attacks and ultimately aiding in enhancing the safety of AI systems. In a landscape where LLMs are increasingly deployed, ensuring the robustness of these models against adversarial threats is critical.

🎯 Why It's Interesting for AI Security Researchers

This research is highly relevant to AI security researchers as it addresses one of the fundamental challenges of securing large language models against adversarial attacks. It provides a practical framework for evaluating model vulnerabilities in a resource-efficient manner, which is particularly important as LLMs become more integrated into applications. By offering insights into the effectiveness of various adversarial techniques and presenting a method that does not require access to model internals, this work contributes to a growing body of literature that seeks to bolster the security and ethical use of AI technologies.

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