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Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks
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Large language models (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
Forward citations
Cited by 4 Pith papers
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AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents
A trajectory-adaptive honeypot system, AgentSnare, achieves a 0/45 verified exploit rate against LLM-based penetration testers across 15 vulnerable web apps and three attacker models.
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Beyond Pattern Matching: Seven Cross-Domain Techniques for Prompt Injection Detection
The work introduces and partially evaluates seven cross-domain prompt injection detectors, reporting F1 gains on benchmarks like deepset/prompt-injections and indirect-injection sets via local alignment, stylometry, a...
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LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems
A systematic review that categorizes LLM threats, severity scores, and mitigations across development and operation life cycles and multiple deployment scenarios.
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Agentic AI and the Cyber Arms Race
A perspective piece arguing that agentic AI will democratize cyberweapons and reshape global power balances like nuclear proliferation did.
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