Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks (2410.20911v2)

Abstract: LLMs are increasingly being harnessed to automate cyberattacks, making sophisticated exploits more accessible and scalable. In response, we propose a new defense strategy tailored to counter LLM-driven cyberattacks. We introduce Mantis, a defensive framework that exploits LLMs' susceptibility to adversarial inputs to undermine malicious operations. Upon detecting an automated cyberattack, Mantis plants carefully crafted inputs into system responses, leading the attacker's LLM to disrupt their own operations (passive defense) or even compromise the attacker's machine (active defense). By deploying purposefully vulnerable decoy services to attract the attacker and using dynamic prompt injections for the attacker's LLM, Mantis can autonomously hack back the attacker. In our experiments, Mantis consistently achieved over 95% effectiveness against automated LLM-driven attacks. To foster further research and collaboration, Mantis is available as an open-source tool: https://github.com/pasquini-dario/project_mantis

• The paper introduces Mantis, a framework that repurposes prompt injection to disrupt automated LLM-driven cyberattacks using agent-counterstrike and agent-tarpit techniques.
• It demonstrates over 95% effectiveness in simulated cybersecurity challenges by coercing LLM agents into actions that compromise their own attack vectors.
• The approach redefines cybersecurity defense by exploiting inherent vulnerabilities in LLMs, thereby offering both proactive countermeasures and resource exhaustion strategies.

Prompt Injection as a Defense Against LLM-Driven Cyberattacks

The paper explores the innovative use of prompt injection to counteract cyberattacks driven by LLMs. As LLMs are increasingly leveraged to automate sophisticated cyberattacks, this research introduces Mantis, a defensive framework designed to exploit the intrinsic vulnerabilities within LLMs, particularly their susceptibility to adversarial prompts.

Overview of Mantis Framework

Mantis capitalizes on the interaction between LLM agents and target systems, using carefully crafted prompt injections to disrupt the agent's attack strategy or even compromise the attacker's system. The framework operates autonomously, deploying decoy services that appear vulnerable to lure LLMs into predictable interaction patterns. Once engaged, Mantis embeds adversarial prompts into the decoy’s responses, which the LLM interprets as legitimate commands. This mechanism serves two primary defense strategies: agent-counterstrike and agent-tarpit.

1. Agent-Counterstrike: This is an active defense strategy where the Mantis framework manipulates the LLM agent into executing commands that compromise the attacker's system. For instance, it may lead the agent to open a reverse shell on their own machine, effectively turning the tables and allowing the defender to gain access to the attacker's environment.
2. Agent-Tarpit: This passive defense strategy focuses on resource exhaustion. The agent is guided into an elaborate and infinite task—such as navigating a fictitious file system—thereby consuming computational resources and operational time without achieving any real progress towards its objectives.

Experimental Results

The efficacy of the Mantis framework is demonstrated across several simulated scenarios using novice-level cybersecurity challenges from HackTheBox. The framework consistently achieved over 95% effectiveness in both preventing the adversary from achieving its goals and fulfilling its own sabotage objectives. Employing LLMs such as GPT-4 and GPT-4-o, Mantis showcases its adaptability to various LLM-driven attack agents, reinforcing its design robustness and its practical applicability against AI-generated threats.

Implications and Future Developments

From a practical perspective, Mantis offers significant advantages in the field of cybersecurity. By leveraging LLMs' weaknesses for defense, it shifts the landscape in favor of defenders who can now utilize the same toolset that attackers rely upon. The framework not only acts as a deterrent by increasing the operational cost and complexity of automated attacks, but it also potentially transforms the dynamics of cybersecurity defense strategies.

Theoretically, this work underscores a crucial paradigm: exploiting model vulnerabilities for defensive purposes. It extends beyond traditional defense models by directly engaging and counteracting automated adversarial actions, thus opening paths for new research on defensive use cases of adversarial machine learning.

Ethical Considerations

The deployment of Mantis raises ethical questions, especially concerning the hack-back elements of agent-counterstrike strategies. While such active defenses can deter future attacks, they also border on legal and ethical boundaries, requiring careful consideration of potential repercussions and adherence to cybersecurity laws and norms.

In conclusion, the research presents a compelling approach to cybersecurity through prompt injection, transforming an often-seen liability into an asset. While the field of AI-driven defense mechanisms is still evolving, the insights and methodologies introduced by Mantis carve a promising path forward, advocating for adaptability and proactive strategies in threat mitigation against LLM-automated cyberattacks.