Clawed and Dangerous: Can We Trust Open Agentic Systems?
Organizations: CSIRO Data61 · University of Technology Sydney
Abstract
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.
Figures & tables
| System | Vendor | Form Factor | Market | Planner | Tools | Memory | Priv. Exec. | 3rd-party Ext. | Reach / Scale |
| Claude Code ( Anthropic, 2026 ) | Anthropic | Desktop coding agent | Global | ✓ | ✓ | ✓ | ✓ | ✓ | 80k |
| Cursor ( Anysphere, 2026 ) | Anysphere | Desktop coding agent | Global | ✓ | ✓ | ▲ | ✓ | ✓ | 32.5k ; 1M+ DAU |
| Windsurf ( Codeium, 2025 ) | Codeium | Desktop coding agent | Global | ✓ | ✓ | ▲ | ✓ | ✓ | 1M+ users |
| Cline ( Cline, 2026 ) | Community | Desktop coding agent | Global | ✓ | ✓ | ▲ | ✓ | ✓ | 2M downloads |
| Aider ( Aider-AI, 2026 ) | Community | Desktop coding agent | Global | ✓ | ✓ | ✗ | ✓ | ✗ | 5.7M PyPI installs |
| Devin ( Cognition AI, 2024 ) | Cognition AI | Cloud SWE agent | Global | ✓ | ✓ | ✓ | ✓ | ▲ | 1,000 companies |
| Runtime security | Governance | Operat. maturity | ||||||||
| Approach | ① | ② | ③ | ④ | ⑤ | ⑥ | ⑦ | ⑧ | ⑨ | ⑩ |
| CaMeL ( Debenedetti et al., 2025 ) | ✓ | ✗ | ✓ | ✗ | ✗ | ▲ | ✗ | ✗ | ✗ | ✗ |
| Fides ( Costa et al., 2025 ) | ✓ | ✗ | ✓ | ✗ | ✗ | ✓ | ▲ | ✗ | ✗ | ✗ |
| Progent ( Shi et al., 2025b ) | ✗ | ✗ | ✓ | ✗ | ✗ | ✓ | ✗ | ▲ | ▲ | ▲ |
| Task Shield ( Jia et al., 2025 ) | ▲ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| GuardAgent ( Xiang et al., 2025 ) | ✗ | ✗ | ✓ | ✗ | ✗ | ▲ | ✗ | ✗ | ▲ | ✗ |
| Group | Paper | Year/Venue | Status | Type | Agent | Concern | Trust Boundary | Lifecycle | Artifact | Evid. |
| Survey & Broad Threat Overview | Taming OpenClaw ( Deng et al., 2026 ) | 2026/arXiv | Pre | Survey | OC | ecosystem threats | Ecosystem-Wide | Design+Deploy+Ops | ecosystem synth. | Sv |
| Navigating Risks ( Gan et al., 2024 ) | 2024/arXiv | Pre | Survey | GA | sec./privacy risks | Ecosystem-Wide | Design+Ops | survey taxonomy | Sv | |
| Trustworthy Agents ( Yu et al., 2025 ) | 2025/arXiv | Pre | Survey | GA | trust modules | Ecosystem-Wide | Design+Ops | modular taxonomy | Sv | |
| Securing Agentic AI ( Narajala and Narayan, 2025 ) | 2025/arXiv | Pre | ThreatModel | EA | enterprise threats | Ecosystem-Wide | Design+Deploy+Ops | threat model | Ps | |
| Benchmark & Empirical Evaluation | BIPIA ( Yi et al., 2025 ) | 2024/MSR | Lab | Benchmark | TA | indirect prompt inj. | Content-Agent | Test | bench suite | Bm |
| InjecAgent ( Zhan et al., 2024 ) | 2024/ACL | P | Benchmark | TA | tool-aided PI | Content-Agent | Test | 1054-case bench | Bm |
| Group | Paper | SE Lesson | Residual Gap |
| Survey & Broad Threat Overview | Taming OpenClaw ( Deng et al., 2026 ) | Ecosystem studies benefit from lifecycle-oriented synthesis | Narrower than a cross-paper SE SoK |
| Navigating Risks ( Gan et al., 2024 ) | Broad surveys need sharper engineering decomposition | Limited operational and deployment detail | |
| Trustworthy Agents ( Yu et al., 2025 ) | Module taxonomies should be complemented with lifecycle taxonomies | Weak release engineering and ops framing | |
| Securing Agentic AI ( Narajala and Narayan, 2025 ) | Threat models need lifecycle mapping and empirical triangulation | Limited evidence synthesis across lifecycle phases | |
| Benchmark & Empirical Evaluation | BIPIA ( Yi et al., 2025 ) | Early benchmarks shaped attention toward content-boundary attacks | Weak deployment realism and ops coverage |
| InjecAgent ( Zhan et al., 2024 ) | Tool-using agents need dedicated evaluation beyond base LLMs | Weak on ops, governance, and provenance |
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
| Stage | Action | Papers |
| Identification | Query results aggregated from all sources | 340 |
| Deduplication | Merged arXiv/DBLP/OpenReview/venue versions | 210 |
| Title/abs screening | Retained clearly relevant papers | 82 |
| Full-text review | Final inclusion with normalized coding | 50 |
LLM Agent Security
Large language model agent security studies attacks and safeguards for systems that plan, use tools, retain memory, and act across applications.
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