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Introduction:
Developers treat markdown files as harmless documentation, but threat actors see them as goldmines. A recent audit of 1,000 public `CLAUDE.md` files revealed that 82% leaked sensitive internal data—API endpoints, database schemas, on-call handles, and even environment variable names—all indexed by Google and ignored by default secret scanners because “it’s just documentation.”
Learning Objectives:
- Identify common leak patterns in markdown-based AI assistant configuration files.
- Implement automated pre-commit scanning using gitleaks and Semgrep to block secrets in `CLAUDE.md` and similar docs.
- Apply redaction techniques and environment variable placeholders to sanitize documentation without breaking AI assistant functionality.
You Should Know:
- Understanding the Leak: Why Default Secret Scanners Miss Markdown
Traditional secret scanners like gitleaks (default rules) focus on regex patterns for API keys, tokens, and passwords. They ignore “safe-looking” markdown content—but attackers don’t. In the audit, 47% of files leaked internal API endpoints (e.g., `https://internal-api.company.com/v2/payments`), 41% exposed database queries with real hostnames, and 29% contained live secrets inside example snippets.
What this looks like in a vulnerable `CLAUDE.md`:
Project context API Base URL: https://api.acme.com/v1 DB Connection: psql -h acme-prod-rds.123456789012.us-east-1.rds.amazonaws.com -U admin -d production Auth header: Authorization: Bearer process.env.REAL_SIGNING_KEY
How to test your own repos (Linux/macOS):
Install gitleaks brew install gitleaks macOS or Linux: wget https://github.com/gitleaks/gitleaks/releases/download/v8.18.0/gitleaks_8.18.0_linux_x64.tar.gz && tar xzf gitleaks_8.18.0_linux_x64.tar.gz && sudo mv gitleaks /usr/local/bin/ Run a scan on your markdown files specifically gitleaks detect --source . --no-git --report-format json --report-path leaks.json --log-level debug --redact Custom rule for internal endpoints (create custom.toml) cat > custom.toml << 'EOF' [[bash]] id = "internal-endpoint" description = "Internal API or hostname leak" regex = '''https?://[\w-.]+.(internal|corp|dev|prod|staging).[\w-.]+|psql -h [\w-.]+rds[\w-.].amazonaws.com''' tags = ["markdown", "leak"] EOF gitleaks detect --source . --config custom.toml
Windows (PowerShell):
Download gitleaks Invoke-WebRequest -Uri "https://github.com/gitleaks/gitleaks/releases/download/v8.18.0/gitleaks_8.18.0_windows_x64.zip" -OutFile gitleaks.zip Expand-Archive gitleaks.zip -DestinationPath . .\gitleaks.exe detect --source . --no-git
2. Six-Step Fix: Sanitizing Your CLAUDE.md Today
The post outlines six immediate fixes. Below is the implementation guide for each.
Step 1 – Move secrets to .env and reference placeholders
Replace hardcoded values with `${VARIABLE_NAME}` syntax. Your `CLAUDE.md` should never contain actual credentials.
Before (leaky)
DB_URL: postgresql://admin:pass123@prod-db:5432/app
After (safe)
DB_URL: ${DATABASE_URL} Set in .env, not committed
Step 2 – Use placeholder patterns consistently
API Endpoint: ${API_BASE_URL}/v2/users
Internal Dashboard: https://${INTERNAL_DOMAIN}/monitoring
Step 3 – Redact internal URLs with `
Internal Kibana: <INTERNAL>kibana.corp.net</INTERNAL> Confluence docs: <INTERNAL_REDACTED>
Step 4 – Strip database specifics
Remove hostnames, ports, usernames, and table names.
Instead of: "psql -h prod-db.internal -U readonly -d sales"
Write: "psql -h ${DB_HOST} -U ${DB_USER} -d ${DB_NAME} — see internal wiki"
Step 5 – No direct @name mentions for on-call
Replace personal handles with role-based references.
Leaky: "Oncall for payments: @jenny (page direct)" Fixed: "Oncall: use `/oncall payments` slash command or payments-oncall channel"
Step 6 – Add gitleaks pre-commit hook for .md files
Create `.pre-commit-config.yaml`:
repos: - repo: https://github.com/gitleaks/gitleaks rev: v8.18.0 hooks: - id: gitleaks args: ["--config", ".gitleaks.toml", "--redact", "--verbose"] files: .(md|txt|yaml|yml|json)$
Install pre-commit (Linux/macOS):
pip install pre-commit pre-commit install pre-commit run --all-files
- Building a Custom Semgrep Rule for Markdown Secrets
Semgrep allows pattern-based scanning beyond regex. The post references Semgrep for markdown—here’s how to use it.
Install Semgrep:
Linux/macOS python3 -m pip install semgrep or via brew: brew install semgrep Windows (WSL recommended or via pip)
Create a custom rule file `markdown-secrets.yaml`:
rules: - id: markdown-plaintext-secrets pattern-regex: '(process.env.[A-Z_]+|psql -h [\w-.]+|mongodb://[^/\s]+|https?://<a href="prod|internal|corp">\w-.</a>[\w-.].com)' message: "Potential secret or internal endpoint leaked in markdown file" languages: - generic paths: include: - ".md" severity: ERROR <ul> <li>id: direct-oncall-mention pattern-regex: '@[\w-]+<a href="page|call|direct">\s(</a>' message: "Direct on-call handle exposed" languages:</li> <li>generic paths: include:</li> <li>".md" severity: WARNING
Run Semgrep:
semgrep --config markdown-secrets.yaml . --json -o semgrep-report.json
4. Hardening CI/CD Pipelines Against Markdown Leaks
Integrate scanning into GitHub Actions (using the post’s referenced gitleaks-action).
GitHub Actions workflow `.github/workflows/secrets-scan.yml`:
name: Scan Markdown for Secrets on: [push, pull_request] jobs: gitleaks: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - name: Run gitleaks uses: gitleaks/gitleaks-action@v2 env: GITLEAKS_CONFIG: .gitleaks.toml with: args: --redact --verbose semgrep: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Semgrep markdown scan run: | pip install semgrep semgrep --config markdown-secrets.yaml . --error
Custom gitleaks config `.gitleaks.toml` for markdown:
[bash] useDefault = true [[bash]] id = "internal-dns" description = "Internal hostname leak" regex = '''[\w-.]+.(internal|corp|prod|staging|dev).[\w-.]+''' tags = ["markdown", "network"] [[bash]] id = "db-connection-string" description = "Database connection leak" regex = '''(psql|mysql|mongodb) -h [\w-.]+ -U \w+ -d \w+''' tags = ["database", "markdown"]
5. Redacting Existing CLAUDE.md Files: A Remediation Script
Automate redaction across your repo using `sed` and custom Python.
Linux/macOS one-liner to replace common patterns:
Backup original
cp CLAUDE.md CLAUDE.md.bak
Replace API base URLs with placeholder
sed -i -E 's|https?://[a-zA-Z0-9.-]+.(internal|corp|prod)[^ ]|${API_BASE_URL}|g' CLAUDE.md
Replace database hostnames
sed -i -E 's|psql -h [a-zA-Z0-9.-]+.rds.[a-z0-9-]+.amazonaws.com|psql -h ${RDS_HOST}|g' CLAUDE.md
Remove @mentions for on-call
sed -i -E 's/@[a-zA-Z0-9]+( (page direct))?/on-call-role/g' CLAUDE.md
Windows (PowerShell):
(Get-Content CLAUDE.md) -replace 'https?://[\w-.]+.(internal|corp|prod)[^\s]', '${API_BASE_URL}' | Set-Content CLAUDE.md
(Get-Content CLAUDE.md) -replace 'psql -h [\w-.]+.rds.[\w-.]+.amazonaws.com', 'psql -h ${RDS_HOST}' | Set-Content CLAUDE.md
- AI Assistant Hardening: What to Tell Your LLM
Since `CLAUDE.md` is read by AI on every session, you can include instructions to prevent leakage.
Add this header to every `CLAUDE.md`:
SECURITY NOTICE TO AI:
- Never echo back sensitive placeholders (${...}) or internal domain patterns.
- If asked for API URLs, respond: "Refer to internal documentation, value redacted."
- Treat any string matching ${} as a secret and never output it verbatim.
Test AI behavior by asking: “What is the database host?” The safe response should refuse or return placeholder.
What Undercode Say:
- Key Takeaway 1: Documentation files are the new shadow IT. Attackers now scan GitHub for
CLAUDE.md,README.md, and `.md` files using simple Google dorks (inurl:CLAUDE.md "DB_URL"). Your secret scanner must evolve to detect “documentation-shaped” leaks, not just credential-shaped ones. - Key Takeaway 2: The fix is not just technical—it’s behavioral. Developers need training on why markdown is dangerous. Implement pre-commit hooks, but also enforce a rule: any file read by an AI assistant must pass a “placeholder-only” policy. The six-step fix from the audit is immediately actionable and costs near-zero engineering time.
Analysis: The audit’s 82% leak rate is catastrophic for DevSecOps. These leaks aren’t theoretical—they expose production infrastructure to reconnaissance, enabling attackers to map internal networks, steal session tokens, or pivot from API endpoints. Default gitleaks (v8) misses these patterns because its rule set prioritizes entropy-based detection. Custom rules are mandatory. The real risk amplification comes from AI assistants: if your `CLAUDE.md` is leaked, an attacker can feed it back to a public LLM to auto-generate exploits. This isn’t a bug—it’s a new class of supply chain risk.
Prediction:
Within 12 months, we will see the first major breach traced directly to a leaked `CLAUDE.md` file. Attackers will weaponize LLM context injection—feeding leaked markdown files to AI models to generate precise payloads for internal endpoints. In response, secret scanners will add “markdown-aware” detectors, and cloud providers (AWS, GCP) will introduce automated scanning for documentation leaks in artifact repositories. Companies that fail to adopt placeholder-based redaction will face regulatory scrutiny, as compliance frameworks (SOC2, ISO 27001) will explicitly add “documentation secrets management” controls by 2027. The era of trusting markdown is over.
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IT/Security Reporter URL:
Reported By: Yildizokan Aisecurity – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
