mlsanityData to 7 Oct 2026

Clawed and Dangerous: Can We Trust Open Agentic Systems?

arXiv 2603.26221 ↗First posted 27 Mar 2026cs.CR cs.AI cs.ETcited by 3

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Open agentic systems combine LLM-based planning with external capabilities, persistent memory, and privileged execution. They are used in coding assistants, browser copilots, and enterprise automation. OpenClaw is a visible instance of this broader class. Without much attention yet, their security challenge is fundamentally different from that of traditional software that relies on predictable execution and well-defined control flow. In open agentic systems, everything is ''probabilistic'': plans are generated at runtime, key decisions may be shaped by untrusted natural-language inputs and tool outputs, execution unfolds in uncertain environments, and actions are taken under authority delegated by human users. The central challenge is therefore not merely robustness against individual attacks, but the governance of agentic behavior under persistent uncertainty. This paper systematizes the area through a software engineering lens. We introduce a six-dimensional analytical taxonomy and synthesize 50 papers spanning attacks, benchmarks, defenses, audits, and adjacent engineering foundations. From this synthesis, we derive a reference doctrine for secure-by-construction agent platforms, together with an evaluation scorecard for assessing platform security posture. Our review shows that the literature is relatively mature in attack characterization and benchmark construction, but remains weak in deployment controls, operational governance, persistent-memory integrity, and capability revocation. These gaps define a concrete engineering agenda for building agent ecosystems that are governable, auditable, and resilient under compromise.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 37 of its 85 references that are in the map. First posted on arXiv 27 Mar 2026.

Builds on

37 of 85 references.

Closest ancestors · 30

Same direction, newest first

  1. 2026PlanTwin: Privacy-Preserving Planning Abstractions for Cloud-Assisted LLM Agents19 Mar
  2. 2026Memory Poisoning Attack and Defense on Memory Based LLM-Agents9 Jan
  3. 2025MemoryGraft: Persistent Compromise of LLM Agents via Poisoned Experience Retrieval18 Dec
  4. 2025WAInjectBench: Benchmarking Prompt Injection Detections for Web Agents1 Oct
  5. 2025Automatic Red Teaming LLM-based Agents with Model Context Protocol Tools25 Sep
  6. 2025MCP-Guard: A Multi-Stage Defense-in-Depth Framework for Securing Model Context Protocol in Agentic AI14 Aug
  7. 2025Searching for Privacy Risks in LLM Agents via Simulation14 Aug
  8. 2025AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agents31 May
  9. 2025Seven Security Challenges in Cross-domain Multi-agent LLM Systems28 May
  10. 2025IP Leakage Attacks Targeting LLM-Based Multi-Agent Systems18 May
  11. 2025WebInject: Prompt Injection Attack to Web Agents16 May
  12. 2025Securing Agentic AI: A Comprehensive Threat Model and Mitigation Framework for Generative AI Agents28 Apr
18 more
  1. 2025WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks22 Apr
  2. 2025Progent: Securing AI Agents with Privilege Control16 Apr
  3. 2025MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits2 Apr
  4. 2025Defeating Prompt Injections by Design24 Mar
  5. 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents24 Mar
  6. 2025Multi-Agent Systems Execute Arbitrary Malicious Code15 Mar
  7. 2025A Survey on Trustworthy LLM Agents: Threats and Countermeasures12 Mar
  8. 2025Red-Teaming LLM Multi-Agent Systems via Communication Attacks20 Feb
  9. 2025RTBAS: Defending LLM Agents Against Prompt Injection and Privacy Leakage13 Feb
  10. 2024The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM Agents21 Dec
  11. 2024LeakAgent: RL-based Red-teaming Agent for LLM Privacy Leakage7 Dec
  12. 2024Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents14 Nov
  13. 2024Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents3 Oct
  14. 2024EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage17 Sep
  15. 2024AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing16 Sep
  16. 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases17 Jul
  17. 2024GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning13 Jun
  18. 2024Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast13 Feb

Other references · 7

  1. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
  2. 2023Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Modelscited by 237
  3. 2026SoK: Agentic Skills -- Beyond Tool Use in LLM Agentscited by 120
  4. 2020Backstabber's Knife Collection: A Review of Open Source Software Supply Chain Attackscited by 57
  5. 2022Taxonomy of Attacks on Open-Source Software Supply Chainscited by 57
  6. 2024AgentOps: Enabling Observability of LLM Agentscited by 46
  7. 2025PrivWeb: Unobtrusive and Content-aware Privacy Protection For Web Agentscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Security of OpenClaw Agents: Fundamentals, Attacks, and Countermeasures
  2. 2026Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw
  3. 2026A Security Analysis of the OpenClaw AI Agent Framework
  4. 2026Security Attack and Defense Strategies for Autonomous Agent Frameworks: A Layered Review with OpenClaw as a Case Study
  5. 2026Toward Securing AI Agents Like Operating Systems
  6. 2026Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats
  7. 2026Security Engineering of OpenClaw: Analyzing Attack Surface Expansion and Trust-Boundary Violations
  8. 2025Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges