# Execution Transcript: Threat Model of threat-patch Skill

## Task Understanding

The task was to produce a structured security threat model of the `threat-patch` skill located at `skills/.experimental/threat-patch/`. The threat-patch skill parses Codex security finding CSVs and generates code patches to remediate vulnerabilities. The user's specific concerns were: (1) what happens if someone feeds it a malicious CSV, and (2) whether the patch workflow has unintended side effects. The threat model was to follow the methodology and output format defined by the `threat-model` skill at `skills/.experimental/threat-model/`.

## Steps Taken

### Step 1: Read the threat-model skill's methodology and references

**Why:** The threat-model skill defines a specific seven-phase methodology and four-section output format that must be followed exactly. Without reading these first, the output would not conform to the expected structure.

**Tools used:** Read tool on four files:
- `skills/.experimental/threat-model/SKILL.md` -- skill overview and workflow
- `skills/.experimental/threat-model/references/methodology.md` -- detailed seven-phase approach
- `skills/.experimental/threat-model/references/output-format.md` -- exact document templates
- `skills/.experimental/threat-model/references/attack-patterns.md` -- technology-specific patterns

**Outcome:** Established the required workflow (Codebase Survey -> Component Mapping -> Asset Identification -> Trust Boundaries -> Attack Surfaces -> Calibration -> Output) and the four-section output format (Overview, Trust Boundaries, Attack Surfaces, Criticality Calibration).

### Step 2: Read all files in the threat-patch skill

**Why:** Phase 1 (Codebase Survey) and Phase 2 (Component Mapping) of the methodology require understanding what the project does, its components, and data flows. Every file in the skill needed to be read to identify all attack surfaces.

**Tools used:**
- Glob to list all files in the threat-patch directory
- Bash (`ls -la`) for directory structure
- Read tool on nine files:
  - `SKILL.md` -- main skill document, workflow overview, key principles, guardrails
  - `config.json` -- configuration (CSV path, threat model path, commit settings)
  - `metadata.json` -- version, type, references
  - `gotchas.md` -- known operational issues
  - `hooks/hooks.json` -- PreToolUse hooks for Edit and Write
  - `references/workflow.md` -- detailed nine-phase patching methodology
  - `references/fix-patterns.md` -- fix templates by vulnerability class
  - `references/output-format.md` -- patch documentation templates
  - `scripts/parse-findings.sh` -- bash/Python CSV parser script

**Outcome:** Complete understanding of the system: its inputs (CSV files, threat models, inline descriptions), processing (parsing, triaging, code reading, patching), outputs (code patches, documentation, git commits), and guardrails (hooks, confirmation gates).

### Step 3: Systematic threat analysis following methodology phases

**Why:** Each phase of the methodology builds on the previous one. I followed them sequentially.

**Phase 1 - Codebase Survey:** Identified the project as an AI agent skill (not a compiled application) comprising markdown documents, JSON config, a bash/Python script, and hook definitions. Deployment model: local, single-user, within Claude Code agent session.

**Phase 2 - Component Mapping:** Identified six components: (1) parse-findings.sh (CSV ingestion), (2) workflow orchestration via SKILL.md (LLM-mediated), (3) code reading via agent tools, (4) patch generation via Edit/Write tools, (5) hooks system, (6) git commit functionality.

**Phase 3 - Asset Identification:** Determined four primary assets: host filesystem integrity, codebase integrity, developer trust in output, git history integrity.

**Phase 4 - Trust Boundaries:** Classified inputs into three tiers: attacker-controlled (CSV fields, inline descriptions, THREAT-MODEL.md content, file contents at referenced paths), operator-controlled (config.json, hooks.json, environment), developer-controlled (SKILL.md, reference docs, parse-findings.sh).

**Phase 5 - Attack Surface Enumeration:** Identified seven attack surface areas:
1. CSV parsing and field injection (parse-findings.sh)
2. Arbitrary file read via finding references (workflow Phase 3)
3. Code injection via patch generation (LLM-mediated)
4. Hook bypass and guardrail weakening (hooks.json)
5. Git history manipulation via commit messages
6. Python code execution in parse-findings.sh
7. Inline finding descriptions and prompt injection

**Phase 6 - Criticality Calibration:** Rated each risk considering deployment context (local interactive use vs. automated CI pipelines).

### Step 4: Write the threat model

**Why:** Phase 7 requires writing the output in the exact four-section format from output-format.md.

**Tools used:** Write tool to create THREAT-MODEL.md at the specified output path.

**Outcome:** Produced a complete threat model with all four sections, seven attack surface subsections with surface/risks/mitigations/attacker-story for each, out-of-scope section, and criticality calibration with scope notes.

### Step 5: Write this transcript

**Tools used:** Write tool.

## Issues Encountered

No blocking issues were encountered. Notable observations:

1. **The skill is mostly markdown, not executable code.** This made the threat model focus primarily on data flow through the LLM agent rather than traditional code vulnerabilities. The only executable component is `parse-findings.sh`, which has a relatively small attack surface.

2. **The primary threat vector is LLM-mediated, not direct code exploitation.** The most dangerous attack paths involve manipulating the agent's behavior through crafted inputs (CSV fields, finding descriptions) rather than exploiting memory corruption or injection in the traditional sense. This required adapting the attack-patterns reference material (which focuses on CLI tools, web apps, and native code) to the agent skill context.

3. **The hooks are advisory, not blocking.** This was a key finding -- the PreToolUse hooks run `echo` commands that display warnings but do not actually prevent tool execution. Their effectiveness depends entirely on whether the harness is configured for interactive approval.

## Output Produced

- **THREAT-MODEL.md**: `/Users/pedroproenca/Documents/Projects/dot-skills/skills/.experimental/threat-model-workspace/iteration-1/eval-2-threat-patch-self-analysis/with_skill/run-1/outputs/THREAT-MODEL.md`
  - Section 1: Overview of threat-patch (deployment model, components, security goals)
  - Section 2: Trust boundaries (4 assets, 4 attacker-controlled inputs, 2 operator-controlled, 2 developer-controlled, 4 assumptions)
  - Section 3: 7 attack surface subsections + out-of-scope section, each with surface/risks/mitigations/attacker-story
  - Section 4: Criticality calibration (1 critical, 3 high, 3 medium, 3 low) with scope note

- **transcript.md**: This file, at `/Users/pedroproenca/Documents/Projects/dot-skills/skills/.experimental/threat-model-workspace/iteration-1/eval-2-threat-patch-self-analysis/with_skill/run-1/transcript.md`
