llava
Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.
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Low
Low-risk findings worth noting
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tessl review fix ./backend/cli/skills/llm-tools/llava/SKILL.mdLow
Low-risk findings.
2 low severity findings. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
The required runtime workflow constructs the LLM prompt from user-supplied free text (e.g., the `--query`/`question`/`prompt` fields shown in SKILL.md), meaning outsider-authored content can be fed into the model context if the operating user didn’t author it.
W012: Unverifiable external dependency detected (runtime URL that controls agent).
What this means
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
Why it was flagged
The README instructs cloning and installing remote code via "git clone https://github.com/haotian-liu/LLaVA" followed by "pip install -e .", which fetches third-party code and runs it during setup, so the repository URL can execute remote code.
- Repository
- synthetic-sciences/openscience
- Commit
52845c3
- Audited
- Security analysis
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