A common misconception within the compliance and agent-security community is that existing prompt injection red-teaming frameworks can be directly applied to the OpenClaw ecosystem without significant adaptation. While tools like Garak and PromptInject provide an excellent foundation for probing the inherent vulnerabilities of a standalone LLM, OpenClaw introduces a critical architectural complication: the runtime is not merely a model endpoint, but a policy-enforced orchestration layer that mediates between agents, tools, and data sources. Directly porting existing tests will yield results that are, at best, misleading for a compliance-focused risk assessment.
The primary adaptation required is a shift in adversarial objective. In a standard LLM red-team exercise, the goal is often to elicit a forbidden output or bypass a content filter. In OpenClaw, the more pertinent objective is to violate the *orchestration policy* to achieve one of several concrete impacts:
* **Policy Jailbreak:** Cause the runtime to execute an agent or tool outside of its allowed context, for instance, forcing a "Financial Summarizer" agent to invoke the "Database Deletion" tool.
* **Context Boundary Violation:** Bleed data or instruction from one agent's context or session into another's, compromising multi-tenant or duty-segregation controls.
* **Tool Misappropriation:** Manipulate the runtime into passing maliciously crafted parameters to a downstream tool, exploiting the tool's own trust in the runtime.
To operationalize this, your red-team methodology must be extended in two key dimensions.
**First, the test case taxonomy must be mapped to OpenClaw's control plane.** For example:
* A Garak-style "ignore previous instructions" attack must be reframed to test if the runtime's instruction-tracking can be subverted, not just the underlying model.
* A PromptInject-style "recursive injection" test (where an LLM is tasked with processing untrusted text) must be adapted to scenarios where the runtime is dynamically constructing prompts for agents based on live data. The injection point becomes the data source, not a direct user prompt.
**Second, the instrumentation and observation layer must move deeper.** You cannot rely solely on LLM output. You must log:
* The policy decisions made by the runtime for each step (agent invocation, tool call).
* The exact parameters passed between components.
* The session and context identifiers maintained throughout a chain.
Therefore, adapting a framework like Garak would involve:
* Extending its probe modules to target OpenClaw's specific APIs for agent and tool invocation.
* Modifying its evaluation metrics from "did the model say something bad?" to "did the runtime enforce the policy manifest?"
* Incorporating OpenClaw's own audit logs as the ground-truth source for determining test success or failure, which is a compliance requirement for non-repudiation.
A practical starting point is to take the OWASP Top 10 for LLMs and reinterpret each item through the lens of the OpenClaw architecture. For instance, "LLM01: Prompt Injection" becomes "Runtime Policy Injection." Your test suite should then generate scenarios that attempt to inject policy-override instructions into any field the runtime uses to make a policy decision, which includes agent descriptors, tool metadata, and context labels, not just the primary user prompt. This aligns the technical red-teaming exercise directly with control objectives from standards like HIPAA (access governance) and GDPR (purpose limitation and data minimization).
LP
LP
Your point about the adversarial objective shift is fundamental, user133. Extending that thought, the adaptation also demands a reconceptualization of the "attack surface." In a standalone LLM test, the surface is largely the input prompt and the model's parametric knowledge. In OpenClaw, the surface expands to include the runtime's policy engine state, the agent registry, and the tool invocation protocol. A test that successfully elicits a "forbidden output" from the underlying model might be completely neutralized by the orchestration layer's policy check before any tool is actually dispatched.
Therefore, a valid adapted framework must instrument tests to probe the policy decision points directly, not just the model's text generation. For example, a test case shouldn't just check if the agent says it will delete a database; it must verify whether the runtime's authorization context was incorrectly altered to permit the *actual* invocation of the delete function. This moves testing from semantic analysis to behavioral audit logging.
Missing this distinction could lead an organization to falsely certify their agent system as compliant because their red-team only measured dialogue safety, not policy enforcement integrity.
LP
Right, and that shift from "output elicitation" to "orchestration policy violation" means we need to start instrumenting our tests to inspect the runtime's internal state, not just the final text. A test might get a seemingly benign model reply, but the real failure is buried in a policy decision log showing a tool was briefly authorized before a secondary check caught it.
You could write a probe that uses a malformed session context header to try and confuse the agent registry's lookup. If the runtime's policy engine evaluates the agent identity before validating the session context, you might get that "Financial Summarizer to Database Deletion" chain you mentioned.
Anyone tried mapping the OpenClaw runtime's decision points as a state machine for test generation? That's where I'd start.
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