# Execution Transcript: Threat Model of threat-patch Skill

**Date**: 2026-03-28
**Task**: Threat model the `skills/.experimental/threat-patch/` skill, focusing on malicious CSV input and unintended side effects of the patch workflow.
**Constraint**: No skill files from `skills/.experimental/threat-model/` were consulted. Analysis performed using default knowledge only.

---

## 1. Task Understanding

The user asked for a threat model of the `threat-patch` skill, which parses Codex security finding CSVs and generates code patches. Two specific concerns were raised:
- What happens if someone feeds it a malicious CSV
- Whether the patch workflow has unintended side effects

The output needed to be a `THREAT-MODEL.md` file saved to a specific path within the eval workspace.

## 2. Steps Taken

### Step 1: Discovery -- Identify all files in the skill

**Tool**: Glob + Bash (ls)
**Why**: Needed to enumerate every file in the skill directory to ensure complete analysis coverage.

**Result**: 9 files identified across the root, `hooks/`, `references/`, and `scripts/` directories.

### Step 2: Read all skill files

**Tool**: Read (9 parallel calls)
**Why**: Needed the complete content of every file to understand the system's behavior, trust boundaries, and potential attack surfaces.

**Files read**:
- `SKILL.md` -- Main skill documentation (75 lines). Describes the workflow, guardrails, output modes.
- `metadata.json` -- Version 0.1.0, discipline "composition", type "automation".
- `config.json` -- Configuration with `findings_csv_path`, `threat_model_path`, `commit_patches` (default false), `branch_prefix`.
- `gotchas.md` -- 3 known gotchas about python3 dependency, commit hash staleness, and grouped fix testing.
- `hooks/hooks.json` -- PreToolUse hooks on Edit and Write tools that echo warning messages.
- `scripts/parse-findings.sh` -- 123-line bash+Python script for CSV parsing.
- `references/workflow.md` -- 9-phase detailed methodology (186 lines).
- `references/fix-patterns.md` -- Fix templates for 10 vulnerability classes (315 lines).
- `references/output-format.md` -- Output documentation templates (164 lines).

### Step 3: Identify trust boundaries

**Tool**: Analysis (no tool call -- reasoning over collected data)
**Why**: Trust boundaries define where untrusted data crosses into trusted processing. This is the foundation of the threat model.

**Identified 4 boundaries**:
1. CSV input -> parse-findings.sh
2. Parsed findings -> AI agent context
3. AI agent -> target codebase (file modifications)
4. User -> config.json

### Step 4: Enumerate attack surfaces and threats

**Tool**: Analysis (reasoning over file contents)
**Why**: For each trust boundary, I analyzed what could go wrong if an attacker controlled the input.

**Key findings**:
- The parse-findings.sh script uses Python's csv module correctly but does no output sanitization
- The hooks in hooks.json are echo-only, non-blocking -- they provide warnings but cannot prevent tool execution
- The workflow instructs the agent to read files from `relevant_paths` without path validation
- Finding descriptions are fed directly into the AI agent's context, creating a prompt injection surface
- The Bash tool is not covered by any hooks, allowing bypass of the Edit/Write guardrails

### Step 5: Calibrate criticality and draft recommendations

**Tool**: Analysis (reasoning)
**Why**: Needed to prioritize threats by likelihood x impact and provide actionable remediation steps.

**Result**: 10 threats identified, rated from Critical (T4: prompt injection) to Low (T3: resource exhaustion). 12 recommendations organized by priority.

### Step 6: Write the threat model

**Tool**: Write
**Why**: Produced the final THREAT-MODEL.md at the specified output path.

### Step 7: Write this transcript

**Tool**: Write
**Why**: Required by the task specification.

## 3. Tools Used

| Tool | Count | Purpose |
|------|-------|---------|
| Glob | 1 | File discovery |
| Bash | 2 | Directory listing, directory creation |
| Read | 9 | Reading all skill files |
| Write | 2 | THREAT-MODEL.md output, this transcript |

## 4. Issues Encountered

None. All files were readable, the directory structure was straightforward, and the skill was well-documented enough to perform a thorough analysis.

## 5. Output Produced

**File**: `/Users/pedroproenca/Documents/Projects/dot-skills/skills/.experimental/threat-model-workspace/iteration-1/eval-2-threat-patch-self-analysis/without_skill/run-1/outputs/THREAT-MODEL.md`

**Structure**:
1. System Description -- overview of the skill and its components
2. Trust Boundaries -- 4 boundaries identified
3. Attack Surfaces -- 6 categories with 10 specific threats (T1-T10)
4. Criticality Calibration -- risk matrix with priority ratings
5. Recommendations -- 12 actionable items grouped by priority (P0-P3)
6. Residual Risks -- risks that persist even with mitigations
7. Summary -- key takeaways

**Key conclusion**: The most critical threat is indirect prompt injection through untrusted CSV content (T4), because finding descriptions are fed into the AI agent's context and the agent then modifies source code. The existing safety hooks are non-blocking echo statements that cannot prevent unreviewed edits.
