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The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks

Daniel Ayzenshteyn, Roy Weiss, Yisroel Mirsky

TL;DR

Novel defense strategies that exploit the inherent vulnerabilities of attacking LLMs are introduced, demonstrating the effectiveness of turning LLM vulnerabilities into defensive strategies against LLM-driven cyber threats.

Abstract

As large language models (LLMs) continue to evolve, their potential use in automating cyberattacks becomes increasingly likely. With capabilities such as reconnaissance, exploitation, and command execution, LLMs could soon become integral to autonomous cyber agents, capable of launching highly sophisticated attacks. In this paper, we introduce novel defense strategies that exploit the inherent vulnerabilities of attacking LLMs. By targeting weaknesses such as biases, trust in input, memory limitations, and their tunnel-vision approach to problem-solving, we develop techniques to mislead, delay, or neutralize these autonomous agents. We evaluate our defenses under black-box conditions, starting with single prompt-response scenarios and progressing to real-world tests using custom-built CTF machines. Our results show defense success rates of up to 90\%, demonstrating the effectiveness of turning LLM vulnerabilities into defensive strategies against LLM-driven cyber threats.

The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks

TL;DR

Novel defense strategies that exploit the inherent vulnerabilities of attacking LLMs are introduced, demonstrating the effectiveness of turning LLM vulnerabilities into defensive strategies against LLM-driven cyber threats.

Abstract

As large language models (LLMs) continue to evolve, their potential use in automating cyberattacks becomes increasingly likely. With capabilities such as reconnaissance, exploitation, and command execution, LLMs could soon become integral to autonomous cyber agents, capable of launching highly sophisticated attacks. In this paper, we introduce novel defense strategies that exploit the inherent vulnerabilities of attacking LLMs. By targeting weaknesses such as biases, trust in input, memory limitations, and their tunnel-vision approach to problem-solving, we develop techniques to mislead, delay, or neutralize these autonomous agents. We evaluate our defenses under black-box conditions, starting with single prompt-response scenarios and progressing to real-world tests using custom-built CTF machines. Our results show defense success rates of up to 90\%, demonstrating the effectiveness of turning LLM vulnerabilities into defensive strategies against LLM-driven cyber threats.

Paper Structure

This paper contains 23 sections, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background & Related Work
  3. Automated Pentesting
  4. LLMs & Their Vulnerabilities
  5. Threat Model
  6. Defence Model
  7. Prevention
  8. Detection
  9. Delay
  10. Evaluation Setup
  11. Single Prompt Setup
  12. Black box Approach
  13. System Prompt Analysis
  14. Best Techniques and Assets Analysis
  15. Discussion
  16. ...and 8 more sections

Figures (7)

  • Figure 1: Simple overview of the attack: An attacker using an LLM-powered tool attempts to access sensitive information by running a command (cat important.txt). The tool responds with a defense message, misleading the attacking LLM and reporting "no vulnerabilities found" to the attacker.
  • Figure 2: A complete taxonomy of goals, strategies, and techniques for defending against LLM-powered cyber agents.
  • Figure 3: Prompt Injection method. The right-most subplot is the minimum across all the models, which is the worst-case defense success rate.
  • Figure 4: Luring Method. The right-most subplot is the minimum across all the models, which is the worst-case defense success rate.
  • Figure 5: No summarizer results, including luring and injection methods across different strategies.
  • ...and 2 more figures