CompressionAttack: Exploiting Prompt Compression as a New Attack Surface in LLM-Powered Agents

Key Points

• CompressionAttack framework exploits prompt compression modules in LLM agents
• Compression modules prioritize efficiency over security, creating vulnerabilities
• Adversarial inputs cause semantic drift, altering intended LLM behavior

Implications

BFSI firms using LLM agents for customer service or risk assessment face new security risks from cost-optimization features.

Action Required

Review prompt compression implementations in AI systems for potential security gaps.