AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways
Reference: Deng, Guo, Han, Ma, Xiong, Wen, Xiang (2025). ACM Computing Surveys 57(7), Article 182. Source file: 3716628.pdf. URL
Summary
This survey organizes the emerging threat landscape of LLM-powered AI agents around four knowledge gaps: unpredictability of multi-step user inputs, complexity of internal execution, variability of operational environments, and interactions with untrusted external entities. It unifies single-agent and multi-agent attack surfaces within a perception/brain/action + agent2agent/agent2env/agent2memory taxonomy.
Concrete threats reviewed include adversarial prompts, prompt injection, jailbreaks, backdoor attacks, hallucination and misalignment, tool-use risks, indirect prompt injection, reinforcement-learning environment attacks, cooperative and competitive inter-agent risks, and long/short-term memory attacks. The authors tabulate defenses (prevention- and detection-based), rate their efficacy, and highlight open directions for robust and trustworthy agents.
Key Ideas
- Four knowledge gaps framing agent security.
- Taxonomy: perception / brain / action / agent2agent / agent2env / agent2memory threats.
- Six categories of prompt-injection attack engineering (naive, escape, context-ignore, fake-completion, multimodal, combined).
- Jailbreak domino effect in multi-agent populations.
- Memory poisoning and indirect prompt injection as underexplored surfaces.
Connections
Conceptual Contribution
- Claim: LLM Agents security should be organised around four knowledge gaps (input unpredictability, internal complexity, environmental variability, untrusted interactions) mapped onto a perception/brain/action + agent2{agent,env,memory} taxonomy.
- Mechanism: Surveys adversarial prompts, prompt injection, jailbreaks, backdoors, hallucination, tool-use risks, indirect injection, RL environment attacks, inter-agent cooperative/competitive risks, memory poisoning; tabulates prevention- vs detection-based defences and rates their efficacy.
- Concepts introduced/used: Prompt Injection, Jailbreak, Backdoor Attacks, Tool Use, Memory Poisoning, Hallucination, Model Context Protocol, LLM Agents, Multi-Agent Systems, Trust and Reputation, Distributed Security, Agent Security
- Stance: survey
- Relates to: Provides the threat scaffolding that MalTool Malicious Tool Attacks deepens at the tool layer; complements lifecycle threats in Survey Of Agent Interoperability Protocols; motivates static-analysis defences like A Language-Based Approach To Prevent DDoS.