← Back to Library

Maatphor: Automated Variant Analysis for Prompt Injection Attacks

Authors: Ahmed Salem, Andrew Paverd, Boris Köpf

Published: 2023-12-12

arXiv ID: 2312.11513v1

Added to Library: 2025-11-11 14:00 UTC

Red Teaming

📄 Abstract

Prompt injection has emerged as a serious security threat to large language models (LLMs). At present, the current best-practice for defending against newly-discovered prompt injection techniques is to add additional guardrails to the system (e.g., by updating the system prompt or using classifiers on the input and/or output of the model.) However, in the same way that variants of a piece of malware are created to evade anti-virus software, variants of a prompt injection can be created to evade the LLM's guardrails. Ideally, when a new prompt injection technique is discovered, candidate defenses should be tested not only against the successful prompt injection, but also against possible variants. In this work, we present, a tool to assist defenders in performing automated variant analysis of known prompt injection attacks. This involves solving two main challenges: (1) automatically generating variants of a given prompt according, and (2) automatically determining whether a variant was effective based only on the output of the model. This tool can also assist in generating datasets for jailbreak and prompt injection attacks, thus overcoming the scarcity of data in this domain. We evaluate Maatphor on three different types of prompt injection tasks. Starting from an ineffective (0%) seed prompt, Maatphor consistently generates variants that are at least 60% effective within the first 40 iterations.

🔍 Key Points

  • Introduction of Maatphor, an automated tool for variant analysis of prompt injection attacks on large language models (LLMs).
  • The tool solves the challenges of generating prompt variants and evaluating their effectiveness, thereby enabling comprehensive security testing.
  • Evaluation of Maatphor demonstrates its ability to consistently create effective prompt variants that significantly enhance the success rate of injection attacks from a previously ineffective baseline.
  • Two evaluation techniques are implemented: string matching and similarity-based evaluation, which allow for flexible and accurate potency assessments.
  • The feedback loop mechanism in the tool enhances iteration efficacy, leading to improved variant generation process.

💡 Why This Paper Matters

The paper introduces Maatphor, a novel automated approach to analyzing and generating variants of prompt injection attacks, marking a significant advancement in the security landscape of LLMs. By effectively increasing the success rate of prompt injections, it assists in identifying vulnerabilities and aids in developing robust defenses against such attacks, making it an essential tool in AI security.

🎯 Why It's Interesting for AI Security Researchers

This paper would be of great interest to AI security researchers as it addresses the increasing threats posed by prompt injection attacks, which exploit LLM vulnerabilities. The introduction of an automated tool like Maatphor enhances the understanding of these risks and helps in the formulation of effective defense strategies, thereby contributing to the overall safety and reliability of AI systems.

📚 Read the Full Paper

📄 View on arXiv