mlsanityData to 14 Oct 2026

OpenClaw PRISM: A Zero-Fork, Defense-in-Depth Runtime Security Layer for Tool-Augmented LLM Agents

arXiv 2603.11853 ↗First posted 12 Mar 2026cs.CRcited by 11

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

also in 1 more
Abstract (arXiv)

Tool-augmented LLM agents introduce security risks that extend beyond user-input filtering, including indirect prompt injection through fetched content, unsafe tool execution, credential leakage, and tampering with local control files. We present OpenClaw PRISM, a zero-fork runtime security layer for OpenClaw-based agent gateways. PRISM combines an in-process plugin with optional sidecar services and distributes enforcement across ten lifecycle hooks spanning message ingress, prompt construction, tool execution, tool-result persistence, outbound messaging, sub-agent spawning, and gateway startup. Rather than introducing a novel detection model, PRISM integrates a hybrid heuristic-plus-LLM scanning pipeline, conversation- and session-scoped risk accumulation with TTL-based decay, policy-enforced controls over tools, paths, private networks, domain tiers, and outbound secret patterns, and a tamper-evident audit and operations plane with integrity verification and hot-reloadable policy management. We outline an evaluation methodology and benchmark pipeline for measuring security effectiveness, false positives, layer contribution, runtime overhead, and operational recoverability in an agent-runtime setting, and we report current preliminary benchmark results on curated same-slice experiments and operational microbenchmarks. The system targets deployable runtime defense for real agent gateways rather than benchmark-only detection.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 10 of its 15 references that are in the map. First posted on arXiv 12 Mar 2026.

Builds on

10 of 15 references.

Closest ancestors · 6

Same direction, newest first

  1. 2026Towards Verifiably Safe Tool Use for LLM Agents12 Jan
  2. 2025LlamaFirewall: An open source guardrail system for building secure AI agents6 May
  3. 2025AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection17 Feb
  4. 2024Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents3 Oct
  5. 2024Security of AI Agents12 Jun
  6. 2023Identifying the Risks of LM Agents with an LM-Emulated Sandbox25 Sep

Other references · 4

  1. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  2. 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 365
  3. 2023Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competitioncited by 68
  4. 2025Indirect Prompt Injections: Are Firewalls All You Need, or Stronger Benchmarks?cited by 18

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 11.

11 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026A Security Analysis of the OpenClaw AI Agent Framework
  2. 2026Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats
  3. 2026SecureClaw: Clawing Back Control of LLM Agents
  4. 2026Local LLM Agents as Vulnerable Runtimes:A Source-Code Audit of the Agent Runtime Layer
  5. 2026Agentao: A Policy-Governed Runtime Harness for Embeddable Tool-Using LLM Agents
  6. 2026Security Engineering of OpenClaw: Analyzing Attack Surface Expansion and Trust-Boundary Violations
  7. 2026Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw
  8. 2026AgentGuard: An Attribute-Based Access Control Framework for Tool-Use LLM-Based Agent