Emerging dangers and defense gaps in large language model agents

LLM agents can orchestrate multiple cybersecurity tasks with precision, ranging from identifying vulnerabilities in large-scale networks to simulating adversarial behaviors for resilience testing.

A new research survey assesses how intelligent AI agents are reshaping digital security and the emerging dangers that come with them. Their study, “A Survey on Agentic Security: Applications, Threats, and Defenses,” provides a sweeping analysis of how large language model (LLM) agents are being deployed in cybersecurity, the vulnerabilities they introduce, and the strategies needed to defend against evolving attacks.

The research positions LLM-driven agents at the core of a major technological shift. Agentic AI systems, models that can plan, act, and adapt autonomously across dynamic environments, are increasingly used for automated penetration testing, malware detection, incident response, and red-team operations. But the same capabilities that make them powerful defenders can also turn them into targets or attack vectors.

The study compiles and categorizes more than 150 recent papers, presenting the first structured taxonomy of agentic security. This taxonomy divides the field into three central pillars: applications, threats, and defenses. The researchers explain that agentic AI differs from traditional static models because these systems are built to sense, reason, and execute tasks continuously. They incorporate external tools, memory, and feedback loops, making them adaptive but also complex to secure.

LLM agents can orchestrate multiple cybersecurity tasks with precision, ranging from identifying vulnerabilities in large-scale networks to simulating adversarial behaviors for resilience testing. The authors note that these agentic frameworks are already reshaping cybersecurity operations by bringing self-governing intelligence to what were once manual or reactive workflows. However, this autonomy introduces unique risks, from unintended escalation of system privileges to manipulation through adversarial prompts or poisoned data streams.

How AI agents can be both defenders and attack surfaces

The study’s threat taxonomy dissects how LLM-based agents can be exploited. The authors identify prompt injection attacks, goal hijacking, environment manipulation, and model exploitation as some of the most pressing threats. Unlike conventional systems, agentic AI operates in open-ended, tool-rich contexts where malicious actors can trigger unintended actions simply through crafted inputs or manipulated APIs.

For instance, in an open cyber environment, an attacker might craft a prompt that causes an autonomous agent to leak sensitive data or execute unauthorized system commands. In other cases, corrupted memory or malicious feedback loops can push the agent to reinforce incorrect behaviors, a phenomenon the paper refers to as “self-propagating vulnerability.”

Another highlighted concern is cross-agent interference, where multiple agents operating within the same ecosystem unintentionally manipulate each other’s states or outputs. Such interference can degrade performance or produce false alerts in critical systems like intrusion detection.

Beyond operational threats, the authors discuss the socio-technical implications of agentic systems, such as algorithmic bias being amplified in autonomous decision-making or attackers exploiting explainability gaps to mask their activity. The paper calls for continuous monitoring, transparency in reasoning processes, and layered access control as key safeguards against cascading AI-driven incidents.

Defensive strategies and the path to secure autonomy

The defense framework presented in the study outlines both preventive and reactive measures to mitigate agentic vulnerabilities. The authors classify these defenses into robustness mechanisms, detection systems, and adaptive governance frameworks.

Robustness mechanisms focus on hardening models through adversarial training, input sanitization, and feedback filtering to block injection attempts before they propagate. Detection systems rely on runtime behavior analysis and policy-based oversight to flag anomalies during autonomous operations. Meanwhile, adaptive governance introduces human-in-the-loop supervision and explainable AI modules to maintain oversight over agents’ evolving decision pathways.

The researchers also highlight the promise of multi-agent defense coordination, where autonomous defenders communicate in real time to share insights on attack vectors. This cooperative defense paradigm, they argue, mirrors biological immune systems, creating collective resilience through diversity and communication.

Another promising line of defense comes from red teaming with agents, where AI systems simulate adversarial tactics to expose weaknesses in other agents or networks. Such automated adversarial testing, already used in cybersecurity simulations, is set to become a cornerstone of future digital defense ecosystems.

To systematize progress, the authors propose a research roadmap for agentic security that emphasizes four key directions:

• Developing unified benchmarks for evaluating LLM-agent robustness.
• Integrating secure memory and safe reasoning architectures.
• Establishing continuous risk assessment pipelines for autonomous systems.
• Promoting international collaboration on AI safety standards.

These goals, they argue, are crucial to ensuring that agentic systems remain reliable partners rather than unpredictable threats within the cybersecurity landscape.