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Skill scan report

cann-review

View on GitHub

100 Critical This skill sends sensitive values to an external host and executes remote code during use. Automated analysis flagged 6 additional risk patterns.

What this skill does

The skill is designed for code review on GitCode, analyzing PRs for memory leaks, security vulnerabilities, and code readability, and posting structured feedback as comments.

github/leoyeai - Data exfiltration - 2.1k stars

ThreatsData exfiltration Data Exfiltration Supply Chain Tool Misuse Privilege Escalation Dangerous Code Execution

Threat analysis

Data exfiltration8 findings

Data Exfiltration5 findings

Supply Chain2 findings

Tool Misuse2 findings

Privilege Escalation1 finding

Dangerous Code Execution3 findings

Skill info

Nameleoyeai/cann-review

Registrygithub

Versione5199b5

PURLpkg:github/LeoYeAI/openclaw-master-skills@e5199b5?skill=cann-review

Stars2.1k

SourceView on GitHub

This skill was flagged with critical risk. Review the findings below before installing.

Assessments (21)

Data exfiltration8 findings CRITICAL

CRITICAL

Data exfiltration via anomity-rules

auto-review-final.sh

local pr_list=$(curl -s -H "Authorization: Bearer $GITCODE_API_TOKEN" \

CRITICAL

Data exfiltration via anomity-rules

auto-review-post-comment.sh

local pr_list=$(curl -s -H "Authorization: Bearer $GITCODE_API_TOKEN" \

CRITICAL

Data exfiltration via anomity-rules

auto-review-single-simple.sh

local pr_list=$(curl -s -H "Authorization: Bearer $GITCODE_API_TOKEN" \

CRITICAL

Data exfiltration via anomity-rules

auto-review-single-v2.sh

local pr_list=$(curl -s -H "Authorization: Bearer $GITCODE_API_TOKEN" \

CRITICAL

Data exfiltration via anomity-rules

auto-review-single.sh

local pr_list=$(curl -s -H "Authorization: Bearer $GITCODE_API_TOKEN" \

CRITICAL

Data exfiltration via anomity-rules

check-mentions.sh

local prs=$(curl -s -H "Authorization: Bearer $TOKEN" \

CRITICAL

Data exfiltration via anomity-rules

simple-review.sh

PR_LIST=$(curl -s -H "Authorization: Bearer $GITCODE_API_TOKEN" \

CRITICAL

Data exfiltration via anomity-rules

MIGRATION.md

curl -H "Authorization: Bearer $TOKEN" \

Data Exfiltration5 findings CRITICAL

MEDIUM

Data Exfiltration via skillspector

check-mentions-simple.py

https://api.gitcode.com/

MEDIUM

Data Exfiltration via skillspector

check-mentions.py

https://api.gitcode.com/

MEDIUM

Data Exfiltration via skillspector

gitcode-api.sh

curl -s -X "$method" \
            -H "Authorization: Bearer $API_TOKEN" \
            "${API_BASE}${endpoint}"
    else
        curl -s -X "$method" \
            -H "Authorization: Bearer $API_TOKEN

MEDIUM

Data Exfiltration via skillspector

test-api.sh

https://api.gitcode.com/

CRITICAL

Data Exfiltration via local-llm-review

auto-review-post-comment.sh

The script uses the $GITCODE_API_TOKEN environment variable in curl commands to authenticate and send data to GitCode's API, which could exfiltrate data if the token is compromised.

Supply Chain2 findings HIGH

HIGH

Supply Chain via skillspector

auto-review-single-v2.sh

curl -s -H "Authorization: Bearer $GITCODE_API_TOKEN" \
      "https://api.gitcode.com/api/v5/repos/$owner/$repo_name/pulls?state=opened&per_page=5")
    
    # 使用 Python 提取 PR 信息
    echo "$pr_list"

HIGH

Supply Chain via skillspector

gitcode-api.sh

curl -s -X "$method" \
            -H "Authorization: Bearer $API_TOKEN" \
            "${API_BASE}${endpoint}"
    else
        curl -s -X "$method" \
            -H "Authorization: Bearer $API_TOKEN

Tool Misuse2 findings HIGH

HIGH

Tool Misuse via skillspector

check-mentions-simple.py

subprocess.run(cmd, shell=True

HIGH

Tool Misuse via skillspector

check-mentions.py

subprocess.run(cmd, shell=True

Privilege Escalation1 finding HIGH

HIGH

Privilege Escalation via local-llm-review

MIGRATION.md

The document mentions 'Access Token' without explaining how it should be securely managed, which could lead to misuse or exposure of sensitive credentials.

Dangerous Code Execution3 findings MEDIUM

MEDIUM

Dangerous Code Execution via skillspector

check-mentions-simple.py

    result = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=10)

MEDIUM

Dangerous Code Execution via skillspector

check-mentions.py

    result = subprocess.run(cmd, shell=True, capture_output=True, text=True)

MEDIUM

Dangerous Code Execution via skillspector

post_comment.py

result = subprocess.run(cmd, capture_output=True, text=True)

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How Anomity governs this at runtime

Scan-time vetting tells you what a skill says it will do. Anomity's Endpoint Sensor sees what agents actually do: it discovers skills alongside every other AI artifact on the endpoint, and runtime governance can allow, deny, or log the tool calls a skill triggers. Policy violations route to your SIEM, Slack, email, or Jira, backed by a queryable 90-day audit trail.

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Methodology and disputes

Every skill is assessed by the Anomity Skill Intelligence engine against its public source; findings indicate risk patterns, not confirmed exploitation. Maintainer of cann-review? Report an issue or request a rescan.

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