Making evil AI bots shoot themselves in the foot
Turns LLMs' prompt injection weakness into a defensive weapon against AI-powered cyberattacks.
i.e. making attacking LLMs hack themselves through crafted prompt responses.
📚 https://arxiv.org/abs/2410.20911
🎯 Original Problem:
LLMs are increasingly automating cyberattacks, making sophisticated exploits accessible to unskilled actors. This eliminates the need for technical expertise, enabling scalable attacks through LLM-agents that can autonomously execute entire attack chains.
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🛠️ Solution in this Paper:
• Mantis: A defensive framework that turns LLMs' prompt injection vulnerability into a defensive asset
• Uses decoy services (fake FTP/web servers) to attract attackers
• When LLM-agent interacts with decoys, Mantis injects crafted prompts that either:
- Lead attacker into endless loops (passive defense)
- Trick them into compromising their own machine (active defense)
• Hides injected prompts from human operators using ANSI escape sequences
• Implements two defense strategies:
- agent-counterstrike: Makes attacker open reverse shells
- agent-tarpit: Traps attacker in infinite filesystem exploration
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💡 Key Insights:
• LLMs' susceptibility to adversarial inputs can be weaponized for defense
• Automated LLM-agents follow predictable patterns in cyberattacks
• Decoy services effectively attract and trap malicious LLM-agents
• Prompt injection becomes more effective after attacker gains initial success
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📊 Results:
• 95% effectiveness against automated LLM-driven attacks
• agent-counterstrike method most reliable with near 100% success
• agent-tarpit maintained ~90% success rate
• FTP decoy more effective than Web-app decoy
• Successfully increased attacker's resource costs in tarpit mode
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