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Skill scan report

i4h-workflow-scene-edit

View on GitHub
14 Low Automated analysis flagged 2 potential risk patterns.

What this skill does

Edit an existing environment's scene in place via a bridge session - move/scale/swap objects, adjust cameras, or tweak task description, success bounds, or randomization. Used for editing scenes or la

github/nvidia - Path Manipulation - 2.8k stars

ThreatsPath Manipulation Command Injection

Threat analysis

Path Manipulation1 finding
Command Injection1 finding

Skill info

Namenvidia/i4h-workflow-scene-edit
Registrygithub
Versiona9ea6f9
PURLpkg:github/NVIDIA/skills@a9ea6f9?skill=i4h-workflow-scene-edit
Stars2.8k

Assessments (2)

Path Manipulation1 finding MEDIUM
MEDIUM

Path Manipulation via local-llm-review

SKILL.md

The skill uses `<env>` as a placeholder in URLs and file paths (e.g., `workflows/agentic/arena/arena/environments/<env>_environment.py`). This could be a risk if the `<env>` placeholder is not properl
Command Injection1 finding LOW
LOW

Command Injection via local-llm-review

SKILL.md

The skill includes shell commands that use variables like `<env>` without explicit validation or sanitization (e.g., `workflows/agentic/arena/run.sh ensure-bridge --env <env> --log <run>/logs/bridge.l

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How Anomity governs this at runtime

Scan-time vetting tells you what a skill says it will do. Anomity's Endpoint Sensor sees what agents actually do: it discovers skills alongside every other AI artifact on the endpoint, and runtime governance can allow, deny, or log the tool calls a skill triggers. Policy violations route to your SIEM, Slack, email, or Jira, backed by a queryable 90-day audit trail.

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Methodology and disputes

Every skill is assessed by the Anomity Skill Intelligence engine against its public source; findings indicate risk patterns, not confirmed exploitation. Maintainer of i4h-workflow-scene-edit? Report an issue or request a rescan.

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