---
title: "Tencent AIG joins ClawScan"
slug: tencent-aig-joins-clawscan
url: https://listedarticles.com/articles/tencent-aig-joins-clawscan
canonical_url: https://openclaw.ai/blog/tencent-aig-joins-clawscan
content_type: announcement
language: en
published_at: 2026-10-02T00:00:00.000Z
updated_at: 2026-10-04T20:09:55.424Z
author: "Patrick Erichsen"
authored_by: human
publisher: "OpenClaw"
publisher_url: https://openclaw.ai
topics: ["Security", "AI", "Open Source", "AI Agents", "Developer Tools"]
license: all-rights-reserved
word_count: 392
reading_minutes: 2
citation: "Patrick Erichsen, OpenClaw. \"Tencent AIG joins ClawScan.\" 2 Oct 2026. https://openclaw.ai/blog/tencent-aig-joins-clawscan (all-rights-reserved)"
# The full text follows. The web page shows an extract and sends readers
# to the source above; quote the citation and link the canonical URL.
---

# Tencent AIG joins ClawScan

> OpenClaw integrates Tencent's AI-Infra-Guard into ClawScan so every skill and plugin uploaded to ClawHub runs through AIG as part of security review, with benchmarks and a feedback loop.

## AIG is now part of ClawHub review

We’ve integrated Tencent’s AI-Infra-Guard (AIG) into ClawScan, the open-source command-line tool that powers security review on ClawHub. Every skill and plugin uploaded to ClawHub now runs through AIG as part of its security review.

## How ClawScan works

Our ClawHub Security Signals paper showed how differently scanners can read the same skill. Each brings its own view of risk, and preserving those differences gives us more evidence to work with.

ClawScan runs AIG and NVIDIA’s SkillSpector security scanner independently on each skill or plugin. An AI judge then reviews both scanners’ findings alongside the uploaded files and makes a final security assessment.

Contributors can test different scanners, AI models, and review instructions against the same benchmark to measure whether a change improves the results.

## What Tencent AIG adds

Tencent describes AIG’s skill scanner as “an LLM-driven multi-stage code audit and vulnerability review pipeline.” It can follow the relationship between a skill’s instructions and the scripts, dependencies, and data flows behind them.

AIG reviews nine categories of risk, from instruction hijacking and memory poisoning to remote payload execution, unauthorized access, persistence, and insecure dependencies.

## Benchmarking the combined system

We worked with Tencent to evaluate ClawScan on a fixed 556-case subset of SkillTrustBench, the public benchmark developed by Tencent and the Chinese University of Hong Kong, Shenzhen.

SkillTrustBench includes benign, suspicious, and malicious skills across nine risk categories. It tests whether scanners catch risky behavior, avoid flagging legitimate skills, and distinguish security flaws from malicious intent.

The evaluation helped us validate the scanner settings and the combined review process. Tencent found that AIG and SkillSpector surfaced different risks even when using the same AI model.

**Across those 556 cases, ClawScan matched 86.9% of the benchmark’s labels and correctly classified 98.6% of malicious cases.**

## Improving both projects together

We now share anonymized cases where the scanners disagree, along with confirmed false positives, with Tencent. These examples feed into regression tests and improvements to AIG’s detection rules and review process. We evaluate those changes through ClawScan, creating a feedback loop that improves both projects.

Keeping this work open lets contributors build on the integration and bring their own evidence to the next round of improvements.

## Acknowledgments

We’re grateful to the Tencent team for contributing their scanner, benchmark, and time to this work, from reviewing individual results to resolving production issues.