YARA rule writing from behavioral observations and TI report analysis — sample-to-rule conversion, condition optimization, performance tuning, and retrohunting on VirusTotal and ANY.RUN. Covers YARA/YARA-X syntax, yarGen automated generation, and production rule deployment.
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Low-risk findings.
2 low severity findings. Worth noting, but not necessarily harmful.
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.
The required workflow includes “AI-assisted mode” (`python3 /opt/yarGen/yarGen.py --ai`) which (by the text description) generates an expanded string set using an AI prompt, and yarGen is fed extracted content from a malware sample directory (`/workspace/samples/<SAMPLE>`), which is outsider-originated free text/strings at runtime.
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.
The skill performs a runtime git clone of remote code and then runs it (git clone https://github.com/Neo23x0/yarGen.git followed by running /opt/yarGen/yarGen.py and --update), so external content from that URL is fetched and executed and is a required dependency.
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