T09 · Insecure Skill Coding Practices
Error
- Location
- group_learning.py:6
- Finding
- Hardcoded Feishu Application Credentials## Vulnerability Details **File Locations**: - `group_learning.py:6-7` - `group_learning.py:15-18` - `analyze.sh:3-5` **Vulnerability Type**: Hardcoded reusable credentials **Risk Level**: High ### Vulnerable Code `group_learning.py:6-7`: ```python APP_ID = "cli_a92b19fbc278dbd6" APP_SECRET = "WFsYhmcEZnRjL4c1ClotIeHhoq5568Sp" ``` `group_learning.py:15-18`: ```python def get_token(): url = "https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal" resp = requests.post(url, json={"app_id": APP_ID, "app_secret": APP_SECRET}) return resp.json().get("tenant_access_token") ``` `analyze.sh:3-5`: ```bash TOKEN=$(curl -s -X POST "https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal" \ -H "Content-Type: application/json" \ -d '{"app_id":"cli_a92b19fbc278dbd6","app_secret":"WFsYhmcEZnRjL4c1ClotIeHhoq5568Sp"}' | python3 -c "import sys,json; print(json.load(sys.stdin).get('tenant_access_token',''))") ``` ### Technical Analysis A reusable Feishu application ID and secret are embedded directly in two distributable source files. Anyone who can download the Skill, inspect a deployed copy, access source-control history, or read a backup can recover the credential without executing the Skill. Sending credentials to Feishu's official authentication endpoint is required to obtain a tenant access token. Embedding a fixed secret in the package is not required and violates least-privilege and secret-management principles. The documented configuration mechanism is not used to obtain credentials. The retrieved tenant token is subsequently placed in an authorization header and used to query Feishu messages: ```python headers = {"Authorization": f"Bearer {token}"} ``` The precise authorization scope cannot be determined from the reviewed files because Feishu application permissions are configured externally. Exploitation is therefore limited by the permissions grante ...[truncated 1318 chars]
- Remediation
- ## Remediation Suggestions 1. Revoke and rotate the exposed Feishu application secret immediately. Removing it from the current files is insufficient because copies and source history may retain it. 2. Remove credentials from both `group_learning.py` and `analyze.sh`, including repository history and release artifacts where feasible. 3. Load credentials from environment variables or an OpenClaw-supported secret store: ```python APP_ID = os.environ.get("FEISHU_APP_ID") APP_SECRET = os.environ.get("FEISHU_APP_SECRET") if not APP_ID or not APP_SECRET: raise RuntimeError("Feishu credentials are not configured") ``` 4. Pass secrets to scheduled executions through a protected runtime environment rather than command-line arguments, where they may be exposed through process listings. 5. Restrict secret-file permissions to the account running the Skill, such as mode `0600`. 6. Grant the Feishu application only the minimum message-reading permissions required for explicitly configured groups. Remove unrelated write and administrative scopes. 7. Implement short request timeouts, status checks, and safe error handling without logging credentials or bearer tokens. 8. Add automated secret scanning to development and release workflows.