What Is ClawScan? OpenClaw’s Security Scanner Explained

Key Takeaways

• ClawScan is OpenClaw’s open-source security scanning framework for agent skills and plugins. It coordinates scanners, preserves their findings, and supports an optional AI judge.
• It powers security review on ClawHub. OpenClaw announced Tencent A.I.G’s integration on October 2, 2026, alongside NVIDIA SkillSpector.
• Several separate tools use the ClawScan name. Check the repository and package publisher before following installation instructions.
• A completed scan is evidence, not a safety guarantee. Findings, scan completeness, permissions, and the exact artifact version all matter.
• Local installation does not necessarily mean offline analysis. Selected scanners and judges can depend on external model providers.

What Is ClawScan?

ClawScan is a command-line framework that runs security checks against AI agent extensions and organizes the results for review. The OpenClaw project maintains it in the openclaw/clawscan repository.

Its role is coordination: different scanners inspect a target, their evidence is retained, and a configured judge can produce a final assessment. Developers can also compare configurations against security benchmarks.

Three names describe different parts of the ecosystem:

• OpenClaw: the agent platform.
• ClawHub: the registry where users discover skills and plugins.
• ClawScan: the scanning framework used in the security review process.

This distinction matters because an extension can change an agent’s instructions, introduce executable code, or request access to sensitive resources. Reviewing ordinary software dependencies alone does not address every risk created by those combinations.

Which ClawScan Are You Looking For?

Search results mix multiple projects with similar names. Their features, commands, and privacy claims are not interchangeable.

EdgeOne ClawScan, clawscan.app, and nickoc/clawscan each document their own behavior. Claims such as zero dependencies, a fixed number of checks, or subscription plans should not be transferred to OpenClaw’s project.

Practical rule: identify the repository owner and package scope before installing anything. A matching product name does not establish matching ownership or functionality.

How Does ClawScan Work?

OpenClaw’s October 2026 announcement describes a layered review process:

1. Independent inspection: Tencent A.I.G and NVIDIA SkillSpector examine the submitted artifact separately.
2. Evidence preservation: their findings remain available for comparison.
3. Contextual judgment: an AI judge considers the scanner evidence alongside the uploaded files.
4. Evaluation: contributors can test scanner, model, and instruction changes against a shared benchmark.

The current repository also provides a clawhub profile combining SkillSpector, A.I.G, a built-in static scanner, and a judge. A local run with one scanner does not reproduce that full configuration.

Why preserve disagreement? Consider a hypothetical reporting skill that reads a spreadsheet and sends a summary to an external service. Network access may be appropriate. Sending unrelated credentials to an undisclosed endpoint would be a different finding.

A useful review connects three things: the advertised task, the requested authority, and the actual behavior. Simply counting suspicious keywords loses that context.

What Security Risks Can It Help Identify?

Coverage depends on the selected scanners and their configuration. NVIDIA documents static and optional LLM analysis, while Tencent’s skill scanner organizes its checks around nine SkillTrustBench risk categories.

These examples summarize documented scanner coverage; they are not a promise that every instance will be detected. NVIDIA SkillSpector and Tencent AI-Infra-Guard remain separate upstream projects with their own capabilities and requirements.

A useful distinction is vulnerability versus malicious intent. A legitimate extension can handle credentials badly. Conversely, a polished extension can deliberately request access unrelated to its stated purpose. Both deserve attention, but the appropriate response differs.

How Accurate Is ClawScan? Read the Benchmarks Carefully

OpenClaw reported the following results on a fixed 556-case subset of SkillTrustBench in its October 2, 2026 announcement:

These are project-reported results for that evaluation setup. They do not establish a universal detection rate for every ClawScan configuration, future attack, or production workload.

The two percentages answer different questions. Overall agreement includes distinctions across the benchmark’s labels; correctly classifying malicious cases describes performance on that subset. Neither number alone establishes the false-positive rate users will experience.

A separate research contribution, ClawHub Security Signals, examined 67,453 public skill versions. It reported that 81.9% of flagged skills were identified by only one scanner, while 0.69% of all skills were flagged by all three scanner families studied.

Crucially, the paper describes its labels as automated registry verdicts rather than human-annotated ground truth. Its purpose is to investigate scanner disagreement, not estimate how many public skills are malicious.

The practical inference: multiple scanners can contribute different evidence. Agreement is useful, but a minority finding should be investigated rather than automatically discarded.

How to Install and Use OpenClaw ClawScan

The following examples follow the project’s current repository documentation. Check compatibility with the release you install, since development documentation can move ahead of published packages.

Install the scoped package:

npm install -g @openclaw/clawscan

Inspect available scanners and profiles:

clawscan scanners
clawscan profiles -v

Scan an explicit skill directory:

clawscan ./my-skill --scanner skillspector --output ./clawscan-results.json

Use the bundled ClawHub profile when its prerequisites are configured:

clawscan ./my-skill --profile clawhub --output ./clawhub-review.json

The current quick start expects Docker for command-backed scanners and judges. Selected scanners and profiles may also require credentials and compatible runtime components.

Before running the full profile:

• Inspect its scanner and judge requirements.
• Use a dedicated copy of the extension under review.
• Keep unrelated documents and production secrets outside the scan target.
• Record the package version, scanner versions, profile, and target revision.

The runtime compatibility documentation describes pinned upstream integrations and compatibility patches. Consequently, changing only a model name or combining a new profile with an old runtime may not produce a valid setup.

How to Read ClawScan Results Correctly

Separate execution status, security findings, and policy decisions.

The scanner documentation specifies that a finished run should report status: completed with complete: true. A canceled, timed-out, or failed run with incomplete evidence is an operational failure. It must not become a clean result simply because a JSON file exists.

When reviewing exported results, follow each scanner’s recorded outputPath to its evidence rather than constructing report filenames yourself.

ClawHub’s public audit labels serve a different purpose:

ClawHub also distinguishes audit status from risk level. A legitimate tool with substantial account authority can require review without being classified as malicious.

Recommended decision process: confirm the scan completed, inspect the actual evidence, establish whether the behavior is necessary, and then decide whether the remaining exposure is acceptable for the intended environment.

Is ClawScan Free, Private, and Fully Local?

OpenClaw’s ClawScan is MIT-licensed open-source software. That does not eliminate the operating costs of selected model APIs, external services, or compute. Avoid assigning subscription prices from similarly named products to this repository.

Privacy depends on the entire configuration. SkillSpector supports optional LLM analysis, and Tencent’s standalone skill scanner documents an API-key-based model workflow. A scanner running on a local machine can still call a remote provider.

Before scanning confidential material, establish:

• Inputs: which files and metadata each scanner reads.
• Destinations: which model endpoints or services it contacts.
• Credentials: which environment variables reach scanners and judges.
• Retention: where reports, logs, and provider-side records persist.

ClawScan’s security policy documents redaction of known secret environment values from results and errors. This is a targeted protection, not proof that every sensitive string in every artifact has been removed. Review reports before sharing them.

There is also a documentation inconsistency worth checking against the installed release: the current README describes Docker execution by default, while SECURITY.md retains wording that scanners and judges are not sandboxed by default. Verify the actual execution boundary rather than assuming either statement applies universally.

Can ClawScan Block Risky Installations Automatically?

The repository documents an integration through OpenClaw’s security.installPolicy.exec boundary. It evaluates staged installation material and returns an allow, warn, or block decision.

However, the integration documentation explicitly requires a compatible OpenClaw host that understands the warn response and pauses for confirmation. It also says no compatible release floor is established until the coordinated host change lands. Do not assume every installed OpenClaw version supports the complete workflow.

The integration distinguishes two boundaries: OpenClaw launches the trusted policy executable, and ClawScan can isolate command-backed scanners separately in Docker. Containerized gateways also require staging paths that the Docker daemon can access. Consult the install-policy documentation before enabling enforcement.

For an initial rollout, a sensible approach is to collect reports, review representative findings, and test failure handling before making the scanner a mandatory installation gate.

Common Mistakes and Better Practices

Mistake: reviewing only the instruction file.

An instruction file can describe harmless behavior while bundled code does something else. Review the complete release, including scripts and dependency changes.

Mistake: treating a previous scan as approval for a new version.

Tie each review to an immutable revision or artifact digest. Reassess when the extension or its dependencies change.

Mistake: treating scanner disagreement as noise.

Inspect the disputed file, instruction, or data flow. The disagreement may reflect different coverage rather than a faulty scanner.

Mistake: interpreting failed scans as zero findings.

Make incomplete evidence visible in automation. Route failures to retry or review instead of silently approving the target.

Mistake: removing a warning without addressing its cause.

For skill authors, improve the underlying behavior: narrow file access, disclose external destinations, avoid unnecessary permissions, and explain required credentials. Clear documentation should describe the real implementation.

Mistake: relying on scanning as the only control.

Keep runtime permissions proportionate to the task. A review can reduce uncertainty before installation; it cannot guarantee every later interaction will be safe.

These practices follow from the documented distinction between scanner evidence, incomplete execution, and the authority an extension receives.

Frequently Asked Questions

Is ClawScan an antivirus?

OpenClaw’s ClawScan is better understood as a framework for reviewing agent extensions. Its security policy explicitly says that scanning does not make a malicious skill safe to install or run.

Is EdgeOne ClawScan the same tool?

No. EdgeOne ClawScan is a separately documented security checkup skill. Its A.I.G cloud-lookup settings and privacy statements belong to that implementation and should not be assumed to configure OpenClaw’s scanning framework.

Who benefits most from ClawScan?

It is particularly relevant to extension publishers reviewing releases, operators assessing third-party additions, and security teams comparing scanner configurations. Individual users can also benefit by reading the evidence attached to registry audits before granting sensitive access.

Conclusion

ClawScan provides a structured way to examine AI agent extensions, compare security signals, and turn findings into review decisions. Its value depends on the selected scanners, the completeness of their evidence, and the permissions ultimately granted to the extension.

Start with the official repository, scan one clearly identified skill, and inspect the findings before connecting the process to automated installation decisions. Keep the target version, runtime, and review configuration together so the result remains understandable and reproducible.