SOURCE-LINKED INTELLIGENCE

CAITLYN: Can LLM Agents Autonomously Synthesize Defenses against Emerging Injection Attacks?

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

Prompt injection attacks on Large Language Model (LLM) agents seek to introduce malicious instructions or content into external text sources retrieved by agents, forcing the underlying LLMs to execute harmful actions outside their benign scope. While current defenses effectively counter known injection attacks, deploying them in LLM agent environments remains challenging due to attack variants and emerging threats. Moreover, existing solutions typically suffer from an inherent trilemma, i.e., a constant trade-off among runtime efficiency, contextual precision, and adaptability. To bridge this

Read original source ↗ Open in workspace

recordType

paper

region

Global