Mantis Framework Introduced to Counter LLM-Driven Cyberattacks
/ 3 min read
Quick take - The article discusses the increasing use of large language models (LLMs) in cyberattacks and introduces Mantis, a new open-source defensive framework designed to counter these threats by leveraging LLM vulnerabilities and employing decoy services and prompt injections to disrupt malicious operations.
Fast Facts
- Large language models (LLMs) are being used to automate cyberattacks, making sophisticated exploits accessible to less technically skilled actors.
- Mantis is a new defensive framework designed to counter LLM-driven cyberattacks by exploiting LLM vulnerabilities, particularly prompt injections.
- The framework employs decoy services to engage attackers early and can initiate passive or active defense responses based on detected threats.
- Mantis has shown over 95% effectiveness in experiments against automated attacks and is available as an open-source tool to encourage further research.
- The framework focuses on exhausting attackers’ resources and guiding them to compromise their own systems, highlighting the need for ongoing innovation in LLM security.
Large Language Models and Cybersecurity
Large language models (LLMs) are increasingly being used to automate cyberattacks. This trend is making sophisticated exploits more accessible to a wider range of actors, including individuals with minimal technical expertise. In response to this evolving threat landscape, a new defensive framework called Mantis has been introduced.
Introducing Mantis
Mantis is designed to counter LLM-driven cyberattacks by leveraging the inherent vulnerabilities of LLMs, particularly their susceptibility to prompt injections. This approach aims to disrupt malicious operations effectively. Upon detecting an automated cyberattack, Mantis can initiate a dual response:
- Passive Defense: This may lead the attacker’s LLM to inadvertently disrupt its own operations.
- Active Defense: Alternatively, it can take more aggressive measures to compromise the attacker’s machine.
The framework employs decoy services, such as fake FTP servers and compromised web applications, designed to engage attackers early in the attack chain. By attracting these LLM-agents, Mantis confirms malicious intent and can then deploy dynamic prompt injections to manipulate their actions.
Effectiveness and Operation
Mantis has demonstrated over 95% effectiveness in experiments against automated LLM-driven attacks and is available as an open-source tool. This availability is intended to promote further research and collaboration in this crucial area. The framework operates autonomously, deploying decoys and prompt injections in real-time based on detected interactions. Mantis seamlessly integrates with genuine services to provide protection without disrupting normal operations.
The mechanics of prompt injection attacks are categorized into direct and indirect types. Mantis redefines these prompt injections as strategic assets for defense rather than mere vulnerabilities. The architecture of Mantis comprises two main components:
- Decoys: Intentionally vulnerable services that attract LLM-agents.
- Injection Manager: This manager coordinates the deployment of prompt injections based on real-time attack detection, generating payloads consisting of execution triggers and target instructions that effectively influence LLM-agent behavior.
Sabotage Objectives and Ethical Considerations
Mantis focuses on two primary sabotage objectives:
- Passive Defense: Aims to exhaust the attacker’s resources and slow their campaign.
- Active Defense: Guides attackers into compromising their own systems.
The framework has been validated using state-of-the-art LLMs, including OpenAI’s ChatGPT-4 and Anthropic’s Claude 3.5. Ethical considerations were also taken into account during the development of Mantis, with experiments conducted in controlled environments to mitigate risks. The evaluation setup for testing Mantis against various LLM-agents and target machines indicates significant results, significantly reducing the success rate of attackers across different configurations and LLMs, particularly evident in beginner-level Capture The Flag (CTF) challenges.
The article concludes by reflecting on the broader implications of Mantis, highlighting the ongoing challenges in LLM security and the need for continued innovation in defensive measures against automated AI threats.
Original Source: Read the Full Article Here
