Toward Secure AI-Powered Penetration Testing Agents: Security Threats, Guardrails, and Architectural Perspectives

• URL: http://arxiv.org/abs/2609.16694v1
• Date: Tue, 15 Sep 2026 06:19:50 GMT
• Title: Toward Secure AI-Powered Penetration Testing Agents: Security Threats, Guardrails, and Architectural Perspectives
• Abstract summary: LLM-powered autonomous agents are transforming the penetration testing space with dynamic, multi-step offensive security.<n>But the ability to have persistent memory, to take actions in the real world, and to do long-horizon reasoning raises qualitatively different security concerns.<n>Existing guardrail mechanisms for conversational AI may not be sufficient to secure autonomous AI pentesting agents accordingly.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM-powered autonomous agents are transforming the penetration testing space with dynamic, multi-step offensive security workflows that require minimal supervision by humans. These agents leverage sophisticated reasoning abilities and external security tools to independently carry out reconnaissance, identify vulnerabilities, devise exploitation plans, and perform post-exploitation operations. But the ability to have persistent memory, to take actions in the real world, and to do long-horizon reasoning raises qualitatively different security concerns than traditional chat-based LLM systems. Existing guardrail mechanisms for conversational AI may not be sufficient to secure autonomous AI pentesting agents accordingly. To address these issues, we carry out a comprehensive security analysis on autonomous AI-penetration testing agents. We systematically analyse representative agent architectures, characterise their trust boundaries and attack surfaces and propose a threat taxonomy that is aligned with the lifecycle and covers LLM lifecycle attacks, agent-architecture attacks and cross-cutting behavioural attacks. We analyse the limitations of existing guardrail mechanisms, identify key research gaps, and discuss future research directions for developing specialised, context-aware, and architecture-aware guardrails to secure next-generation AI-driven offensive security systems.

Related papers

• LLM-Centric Agentic AI for UAV Swarms: Architecture, Enabling Technologies, and Open Problems [4.4579151410787246]
  Uncrewed Aerial Vehicle (UAV) swarms have significant potential for applications such as Search and Rescue (SAR) and environmental monitoring.<n>Agentic Artificial Intelligence (AI) represents a shift from standalone Large Language Model (LLM) toward closed-loop cognitive architectures.<n>This paper proposes LLM-Centric Agentic AI for UAV Swarms (LAUS) and reviews key enabling technologies.
  arXiv  Detail & Related papers  (2026-07-05T13:08:06Z)
• AgentLens: Interpretable Safety Steering via Mechanistic Subspaces for Multi-Turn Coding Agent [53.82005364479556]
  Coding agents based on large language models (LLMs) demonstrate remarkable autonomous capabilities.<n>Existing safety mechanisms mainly rely on external guardrails.<n>We propose AgentLens, a white-box defense framework that performs runtime safety detection and representation-level mitigation.
  arXiv  Detail & Related papers  (2026-06-21T21:13:55Z)
• Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security [57.35851886874902]
  Agentic AI systems execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness.<n>This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployments.<n>For each dimension, we clarify key concepts, identify where risks emerge along the agent workflow, and summarize stage-targeted mitigation strategies.
  arXiv  Detail & Related papers  (2026-05-17T10:26:37Z)
• Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms [61.29786895012393]
  Vision-Language-Action (VLA) models are emerging as a unified substrate for embodied intelligence.<n>This survey provides a unified and up-to-date overview of safety in Vision-Language-Action models.
  arXiv  Detail & Related papers  (2026-04-26T15:58:19Z)
• Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses [168.50301366360344]
  Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in safety-critical environments.<n>Unlike digital AI systems, embodied agents must act under uncertain sensing, incomplete knowledge, and dynamic human-robot interactions.<n>This survey provides a comprehensive review of safety research in embodied AI, examining attacks and defenses across the full embodied pipeline.
  arXiv  Detail & Related papers  (2026-03-28T13:21:44Z)
• SoK: The Attack Surface of Agentic AI -- Tools, and Autonomy [3.340255811686752]
  Recent AI systems combine large language models with tools, external knowledge via retrieval-augmented generation (RAG)<n>This agentic AI paradigm greatly expands capabilities - but also vastly enlarges the attack surface.<n>We develop a comprehensive taxonomy of attacks spanning prompt-level injections, knowledge-base poisoning, tool/plug-in exploits, and multi-agent emergent threats.
  arXiv  Detail & Related papers  (2026-03-24T08:21:51Z)
• The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey [46.278608570660005]
  AI agents that combine large language models with non-AI system components are rapidly emerging in real-world applications.<n>This paper presents the first systematic and comprehensive survey of AI agent security.
  arXiv  Detail & Related papers  (2026-03-11T05:25:55Z)
• Securing AI Agents in Cyber-Physical Systems: A Survey of Environmental Interactions, Deepfake Threats, and Defenses [2.6726842616701703]
  This survey provides a comprehensive review of security threats targeting AI agents in cyber-physical systems.<n>We focus on environmental interactions, deepfake-driven attacks, and MCP-mediated vulnerabilities.<n>We quantitatively illustrate how timing, noise, and false-positive costs constrainable defenses.
  arXiv  Detail & Related papers  (2026-01-28T02:33:24Z)
• Securing Agentic AI Systems -- A Multilayer Security Framework [0.0]
  Securing Agentic Artificial Intelligence (AI) systems requires addressing the complex cyber risks introduced by autonomous, decision-making, and adaptive behaviors.<n>Existing AI security frameworks do not adequately address these challenges or the unique nuances of agentic AI.<n>This research develops a lifecycle-aware security framework specifically designed for agentic AI systems.
  arXiv  Detail & Related papers  (2025-12-19T20:22:25Z)
• OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows [77.95511352806261]
  Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.<n>We propose OS-Sentinel, a novel hybrid safety detection framework that combines a Formal Verifier for detecting explicit system-level violations with a Contextual Judge for assessing contextual risks and agent actions.
  arXiv  Detail & Related papers  (2025-10-28T13:22:39Z)
• Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges [14.546961299604554]
  Agentic AI systems powered by large language models (LLMs) are emerging as powerful, flexible platforms for automation.<n>Their ability to autonomously execute tasks across web, software, and physical environments creates new and amplified security risks.<n>This survey outlines a taxonomy of threats specific to agentic AI, reviews recent benchmarks and evaluation methodologies, and discusses defense strategies.
  arXiv  Detail & Related papers  (2025-10-27T21:48:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.