boost-modules
Fail
Audited by Gen Agent Trust Hub on Feb 16, 2026
Risk Level: HIGHPROMPT_INJECTIONDATA_EXFILTRATIONCOMMAND_EXECUTIONEXTERNAL_DOWNLOADS
Full Analysis
- [PROMPT_INJECTION] (HIGH): The skill facilitates Indirect Prompt Injection (Category 8).
- Ingestion points:
chat.messageandrequests.get(url).textin the URL Reader example. - Boundary markers: Weak
<content>tags provide insufficient isolation. - Capability inventory: Access to
llm.chat_completionandllm.stream_final_completion. - Sanitization: None provided.
- Role Manipulation: The 'pirate' example shows how to inject system-role messages into the chat history.
- [DATA_EXFILTRATION] (MEDIUM): Templates include logic for making arbitrary network requests.
- Evidence: Use of
requests.get(url)allows fetching data from non-whitelisted external domains, which can be leveraged for SSRF. - [COMMAND_EXECUTION] (MEDIUM): Facilitates the creation and loading of dynamic Python logic.
- Evidence: The core purpose is writing
apply()functions that the Harbor Boost proxy executes. This pattern (Category 10) is risky if modules are generated or modified by an agent. - [EXTERNAL_DOWNLOADS] (LOW): References non-trusted GitHub repositories and Docker images.
- Evidence:
ghcr.io/av/harbor-boost:latestand related source links point to an untrusted account ('av'). - [INFO] (SAFE): The 'logger.info' scanner alert is a false positive.
- Evidence: The scanner misidentified a Python logging method as a malicious domain.
Recommendations
- AI detected serious security threats
- Contains 1 malicious URL(s) - DO NOT USE
Audit Metadata