Topic
defenses
Artificial Intelligence #llm#security
AutoDojo: Adaptive Attacks Expose Superficial Defenses and Structural Limits in LLM Agents
The AutoDojo framework adaptively optimizes indirect prompt injections against LLM agent defenses, revealing that many current defenses are superficial. Against a filter that reduces static attack success rate to 0%, AutoDojo recovers 28% overall and 64% on action-open tasks due to a structural limitation where injections can pose as ordinary data.
Jun 16, 2026 1 source
Artificial Intelligence #agentic security#ai security
New Survey Maps Agentic Security: Applications, Threats, and Defenses for Autonomous AI
A new survey from arXiv provides the first holistic overview of agentic security, covering how LLM-based agents are used in cybersecurity, their vulnerabilities, and countermeasures. The analysis of over 260 papers reveals that agentic systems are structurally fragile and require defenses spanning the full agent lifecycle.
Jun 16, 2026 1 source