Ai agents under threat: A survey of key security challenges and future pathways

Analysis

Why This Paper Matters

As AI agents become more autonomous and are deployed in critical applications—from robotics to financial trading—their security vulnerabilities become paramount. Unlike traditional ML models, agents operate in dynamic environments, make sequential decisions, and often have memory and tool-use capabilities. This survey is timely because it consolidates scattered security research into a coherent framework organized around four knowledge gaps: (1) perception and input integrity, (2) memory and state manipulation, (3) planning and reasoning robustness, and (4) execution and action safety. By highlighting these gaps, the paper provides a roadmap for both researchers and practitioners to prioritize defenses.

Technical Contributions

The paper's main innovation is its structured taxonomy of AI agent security challenges, which goes beyond typical adversarial ML surveys. Key contributions include:

• Knowledge Gap Identification: Four gaps that cover the entire agent pipeline (perception → memory → planning → execution).
• Threat Mapping: Each gap is linked to specific attack types (e.g., adversarial perturbations for perception, memory poisoning for memory, reward hacking for planning).
• Comparison with Prior Surveys: Unlike [229] which only covers memory mechanisms, this work takes a holistic security view.
• Future Pathways: Suggests concrete research directions such as certified robustness for agent policies and formal verification of memory integrity.

Results

The paper does not present experimental results; it is a survey. Its main output is a comprehensive table (Table 1) that maps each knowledge gap to existing attack literature and defense proposals. The survey covers approximately 200 references, with a focus on works from 2020–2025. No quantitative comparisons (e.g., attack success rates or defense efficacy) are provided, which is typical for a survey paper.

Significance

This survey fills a critical void in the AI safety literature. As agents are increasingly deployed with long-term memory and tool-use (e.g., AutoGPT, Voyager), understanding their unique attack surface is essential. The paper's taxonomy can directly inform the design of more robust agent architectures, such as incorporating input sanitization, memory encryption, and adversarial training for planning modules. For Neura Market's audience of AI practitioners, this work offers a checklist of security considerations when building or deploying agentic systems.