ai-security-assessmentlisted

# AI Security Assessment Turn a concrete AI attack path into a reproducible assessment and remediation test. [threat-model](../threat-model/SKILL.md) owns architectural analysis, [security-review](../security-review/references/code-review/GUIDE.md) owns source and dependency review, and [agent-evaluation](../agent-evaluation/SKILL.md) owns repeated-trial comparisons. Use PyRIT as the execution framework through a project-local `uv` environment. ## Workflow 1. **Record the boundary**: identify the application revision, model, retrieval sources, actors, tools, and granted authority in the [assessment record](templates/assessment.md). Reuse established target, data, action, and cost authorization; proceed autonomously within it. Keep customer evidence in the assessment project. 1. **Choose plausible scenarios**: read [attack cases](references/attack-cases.md). For each case name the attacker-controlled surface, required access, protected asset, and forbidden outcome. Include legitimate autonomous actions that must continue to succeed. 1. **Observe the application**: capture attempted tool calls and resulting state through the real enforcing boundary. Use disposable records, synthetic secrets, and controlled destinations when these preserve the attack path. A direct model endpoint cannot establish the safety of an application's retrieval or tools. 1. **Prepare PyRIT**: follow [PyRIT execution](references/pyrit.md) for the released API, target adapter, scenario selection, local