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Security Review Protocol

Community
dianyike
security-review-protocol

Load when performing dual-source security review, cross-validating Semgrep findings with Codex, or resolving conflicting vulnerability assessments. Trigger on: security audit, scan vulnerabilities, cross-validate findings, conflict resolution, confidence scoring.

Overview

Publisherdianyike
Repositoryclaude-code-insights
Skill namesecurity-review-protocol
Stars
69
Forks
13
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by dianyike on GitHub. Read the source before you install it.

Installation

Install the Security Review Protocol AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/dianyike/claude-code-insights/tree/main/examples/security-reviewer/skills/security-review-protocol
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dianyike/claude-code-insights.git /tmp/claude-code-insights
mkdir -p .claude/skills
cp -r /tmp/claude-code-insights/examples/security-reviewer/skills/security-review-protocol .claude/skills/security-review-protocol
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Security Review Protocol in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Security Review Protocol on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Security Review Protocol is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Security Review Protocol

Core business logic for the dual-verification security review workflow: cross-validation, conflict resolution, and confidence scoring.

Additional resources

1. Cross-Validation Logic

1.1 Finding Normalization

Before comparing, normalize findings from both sources into a common format:

{
  id: "<unique-id>",
  source: "semgrep" | "codex",
  type: "<vulnerability-type>",     // e.g., "sql-injection", "xss", "hardcoded-secret"
  file: "<file-path>",
  line: <line-number>,
  severity: "CRITICAL" | "HIGH" | "MEDIUM" | "LOW",
  description: "<what the issue is>",
  evidence: "<specific code or pattern that triggered this>",
  trigger: "<how it can be exploited>",
  fix: "<remediation suggestion>"
}

1.2 Matching Algorithm

Two findings from different sources are considered matching when:

  1. Same file (exact path match)
  2. Overlapping location (line numbers within 5 lines of each other)
  3. Same vulnerability category (e.g., both are injection-related, both are auth-related)

If criteria 1+3 match but lines differ by >5, flag as partial match — may be the same root cause manifesting at different points.

1.3 Classification

Semgrep FoundCodex FoundClassificationAction
YesYesConfirmedReport with high confidence
YesNoBaseline-onlyReport — tool findings are reproducible
NoYesCodex-onlyVerify with targeted Grep/AST before reporting
NoNo(not applicable)—

2. Conflict Resolution Protocol

2.1 Divergence Types

Type A: Codex says safe, Semgrep says vulnerable (most dangerous)

Conservative stance: treat as vulnerable until proven otherwise.

Resolution steps:

  1. Read the flagged code and its surrounding context (50 lines)
  2. Check if there are sanitization functions upstream
  3. Run a targeted custom Semgrep rule for the specific pattern
  4. If still ambiguous → report as finding with note "Codex disagrees — needs human review"

Type B: Codex says vulnerable, Semgrep says nothing (potential false positive)

Moderate stance: verify before reporting.

Resolution steps:

  1. Use Grep to find the exact pattern Codex described
  2. Use get_abstract_syntax_tree to verify the code structure
  3. If Codex's reasoning is sound and code confirms → report as finding
  4. If pattern not found or reasoning flawed → discard with note

Type C: Both found it but disagree on severity or fix

Resolution steps:

  1. Prefer Semgrep's CWE/severity mapping (based on established rule databases)
  2. Evaluate Codex's reasoning for severity — may have project-specific context
  3. Report with the higher severity (conservative) and note the disagreement

2.2 Deep Verification Techniques

When conflicts arise, use these targeted verification methods:

TechniqueToolWhen to Use
Targeted grepGrepVerify specific code patterns exist
AST analysisget_abstract_syntax_treeVerify code structure matches vulnerability pattern
Custom Semgrep rulesemgrep_scan_with_custom_ruleTest a specific hypothesis about a vulnerability
Upstream traceRead + GrepCheck if input is sanitized before reaching the flagged point
Codex follow-upcodex-replyAsk Codex to explain or defend its finding with evidence

3. Confidence Score Calculation

3.1 Base Weights

SourceWeightRationale
Semgrep finding60%Deterministic, reproducible, based on established rules
Codex finding40%Reasoning-based, can catch logic flaws that rules miss

3.2 Score Formula

confidence = base_score + modifiers

Where:
  base_score:
    - Both agree:        max(60, 40) + 20 = 80%
    - Semgrep only:      60%
    - Codex only:        40%

  modifiers:
    - Deep verification confirms:           +15%
    - Semgrep rule has CWE reference:       +5%
    - Codex provided exploit scenario:      +5%
    - Historical finding exists (platform): +10%
    - Conflict unresolved:                  cap at 50%

  cap: 95% (never claim absolute certainty)

3.3 Escalation Threshold

  • Confidence >= 70%: Report as confirmed finding
  • Confidence 50-69%: Report with "needs verification" flag
  • Confidence < 50%: Escalate to human review, do not auto-report as confirmed

4. Fix-Verify Loop

Boundary: This section is ONLY executed via /security:fix in the main conversation. The security-reviewer subagent and all gate hooks MUST stop at Section 3. Review (Steps 1-3) is read-only; remediation (Step 4) requires explicit user opt-in.

After findings are confirmed (confidence >= 50%), enter the fix-verify loop to remediate and validate fixes.

4.0 Mode Boundary

Sections 1-3 and Section 4 operate under fundamentally different structural guarantees.

  • Sections 1-3 (diagnostic): dual-source cross-validation with weighted scoring. Both Semgrep and Codex contribute findings independently, and the 60/40 weights govern classification.
  • Section 4 (remediation): each round must have exactly one strategy source. Usually that source is Codex; if 4.1 matches a canonical fix pattern, the source becomes the canonical pattern instead. In either case, Semgrep's role narrows to violation oracle — it can confirm "this rule still fires" or "this rule stopped firing," but it cannot confirm that the root cause is resolved or that the fix direction is correct.

The 60/40 diagnostic weights do NOT apply here. The constraints below exist to compensate for this single-proposer asymmetry — they are not bureaucracy.

4.1 Known Fix Gate

Before asking Codex for a fix strategy, check if the finding matches a canonical fix pattern:

CWE CategoryCanonical FixDo NOT
SQL Injection (CWE-89)Parameterized queries / prepared statementsString escaping, concat-based sanitization
XSS (CWE-79)Contextual output encoding / safe sink APIsManual regex stripping
Hardcoded Secret (CWE-798)Remove + rotate + env var / secret managerObfuscation, base64 encoding
Path Traversal (CWE-22)Allowlist + resolve-then-check canonical pathBlacklist patterns like ../

If match found:

  1. Apply the canonical fix
  2. Still write the prediction block (see 4.2 Constraint 2) — the strategy source changes from Codex to canonical pattern, but the verification contract is identical
  3. Open a read-only Codex session (mcp__codex__codex) with the code + applied canonical fix for thread continuity — this ensures codex-reply is available for 4.3 verification
  4. Skip to 4.3

If no match → proceed to 4.2.

Rationale: For well-understood vulnerability classes, letting the model "be creative" only increases drift probability. The prediction block is not "for Codex" — it's the falsifiable target that makes verification meaningful.

4.2 Request Fix from Codex

For same-thread refinement, use codex-reply with the existing threadId to request a fix. If 4.4 requires a fresh strategy reset, open a new read-only mcp__codex__codex session using the fresh fix-strategy template in reference/mcp-tools.md. Two hard constraints apply either way:

Constraint 1 — Single Root-Cause Hypothesis

Each round targets exactly ONE root-cause cluster. A single root cause may manifest across multiple call sites — fixing them together in one round is correct.

What's prohibited: mixing unrelated fix directions in one round (e.g., fixing SQL injection AND XSS simultaneously). This isolates cause-effect so that Semgrep evidence is interpretable.

Constraint 2 — Prediction Block (MANDATORY before applying any fix)

Before applying the fix, write down the following prediction. This applies to BOTH the canonical path (4.1) and the Codex path (4.2):

prediction:
  root_cause_hypothesis: "<what you believe is the underlying cause>"
  minimal_change_scope: "<which files:lines will be modified>"
  expected_rules_to_clear: ["<semgrep-rule-id-1>", ...]
  expected_grep_patterns_to_disappear: ["<pattern>", ...]
  possible_new_findings: ["<rule-id or 'none'>"]
  disconfirming_evidence: "<what result would prove this hypothesis WRONG>"
  rollback_trigger: "<specific condition that means revert immediately>"

If the prediction cannot be stated concretely, the fix is too vague to apply. Stop and refine the hypothesis first.

For MCP prompt templates, see reference/mcp-tools.md.

4.3 Apply and Re-verify

After applying the fix:

  1. Run Semgrep on modified files — record the actual output
  2. Compare actual output against prediction block point-by-point:
    • Did expected_rules_to_clear actually clear? (Y/N per rule)
    • Did expected_grep_patterns_to_disappear actually disappear? (Y/N per pattern)
    • Did any findings appear in possible_new_findings? Expected or unexpected?
    • Did the disconfirming_evidence condition trigger?
  3. Call Codex verify (using the verify prompt from reference/mcp-tools.md) — Codex must compare against the prediction block, not just re-review the code
  4. Classify the round outcome:
    • Prediction confirmed: all expected rules cleared, no unexpected findings → finding resolved
    • Prediction partially confirmed: some rules cleared, others persist → record as hypothesis refinement evidence, proceed to next round. Constraint: partially confirmed only permits same root-cause family refinement. It does NOT permit switching to a new remediation direction in the same thread without recording the original hypothesis as failed or superseded
    • Prediction falsified: disconfirming evidence triggered, or zero predictions matched → this is a hypothesis failure, not an implementation bug

Key framing: Semgrep checks "does the violation pattern still match?" — NOT "is the code now secure."

4.4 Iteration Protocol

Cap at 3 rounds. Each round escalates the strategy diversity requirement:

  • Round 1 fails: Stay on same Codex threadId. Inject into the prompt: the failed prediction block, actual Semgrep output, and the specific mismatch. Ask Codex to revise its root-cause hypothesis, not just tweak the fix
  • Round 2 fails: MANDATORY strategy switch:
    • Abandon the current threadId — start a fresh Codex session
    • New prompt MUST include: both failed hypotheses, their predictions, actual outputs, and the explicit instruction: "propose a fundamentally different approach — the previous two attempts targeted [X] and both failed because [Y]"
    • Purpose: break narrative inertia from accumulated thread context
  • Round 3 fails: Stop all automated repair. Escalate to human with a structured bundle:
    • All 3 hypotheses with prediction blocks
    • All 3 actual Semgrep outputs
    • All 3 prediction-vs-actual comparisons
    • Agent's assessment of why convergence failed

4.5 Rollback Rules

NOT a blanket "any new finding → revert." Apply tiered judgment:

ConditionActionReason
New HIGH/CRITICAL finding not in possible_new_findingsImmediate rollbackUnacceptable regression
Original rule still fires AND zero predictions matchedRollbackHypothesis was entirely wrong, no value in keeping the change
Rule transferred (old cleared, new appeared at same severity)Mark as hypothesis failure, keep change, investigate in next roundMay be progress toward correct direction
Low-severity secondary signal appearedNote in ledger, continueSemgrep is not a truth oracle; minor noise is expected

4.6 Hypothesis Ledger

Each round's hypothesis, prediction, actual output, and decision MUST be persisted — not just held in transient context.

Location: Append as ## Fix-Verify Hypothesis Log as the final section of the security report, after ## Supply Chain.

Format per round:

markdown
### Round N — [CONFIRMED | PARTIALLY CONFIRMED | FALSIFIED | ROLLBACK]
- **Hypothesis**: ...
- **Prediction**: [paste prediction block]
- **Actual Semgrep output**: [rule IDs fired / cleared]
- **Prediction match**: [point-by-point comparison]
- **Decision**: [applied / rolled back / escalated]
- **Evidence carried forward**: [what the next round should know]

Purpose: prevent the agent from repeating the same class of mistake, and give the human reviewer a diagnostic trail if escalation occurs.

4.7 Common Fix Pitfalls

PitfallExamplePrevention
Fix introduces new vulnEscaping SQL by string concat instead of parameterized querySemgrep re-scan catches pattern
Fix is cosmetic onlyRenaming a variable but not fixing the logicCodex re-verify catches intent mismatch
bare except: catches SystemExitsys.exit() inside try/except: blockCheck that except clauses use except Exception: not except:
Fix for wrong versionChecking latest when user specified @^1.0.0Always resolve to exact version before checking
Partial fixFixed one call site but same pattern exists elsewhereGrep for the pattern project-wide after fixing
Hypothesis driftRound 1 targets SQL injection, Round 2 silently shifts to input validationPrediction block comparison catches scope change

5. Gotchas

  • Semgrep semgrep_scan requires absolute paths — relative paths silently return no results
  • Codex may hallucinate line numbers — always verify with Read before citing
  • Semgrep supply chain scan reads lockfiles from CWD — make sure CWD is project root
  • Codex read-only sandbox prevents it from running verification scripts — you must do that yourself
  • If Codex returns a threadId, save it. For same-thread refinement and conflict follow-up, use codex-reply with that threadId. Exception: when 4.4 requires a strategy reset, start a fresh session and save the new threadId
  • Semgrep findings JSON structure may vary between local scan and platform findings — normalize before comparing
  • Never declare "all issues fixed" after applying Codex's suggestions without re-running both Codex verify AND Semgrep re-scan — Codex can confirm fixes that are actually incomplete
  • When Codex says "no new issues", still run Semgrep — Codex misses structural issues like bare except: catching SystemExit, pipefail interactions, and shell quoting edge cases
  • Codex is strong at finding logic-level issues (fail-open, parser bypass, state machine gaps) but weak at shell/bash edge cases (SIGPIPE, IFS, grep | head under pipefail) — Semgrep is better for the latter
  • After each fix round, explicitly list what was claimed fixed vs what was actually verified — prevents "fixed" claims from compounding without evidence
  • Iterate this section: After each real review, append new gotchas here — false positive patterns you encountered, specific Codex hallucination tendencies (e.g., inventing middleware that doesn't exist), or Semgrep rules that consistently misfire on your codebase. This list should grow with use, not stay static

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Security Review Protocol AI skill do?

Load when performing dual-source security review, cross-validating Semgrep findings with Codex, or resolving conflicting vulnerability assessments. Trigger on: security audit, scan vulnerabilities, cross-validate findings, conflict resolution, confidence scoring.

Why use Security Review Protocol on TypingMind?

Because you install it once and use it with any model. Security Review Protocol is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Security Review Protocol in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dianyike/claude-code-insights/tree/main/examples/security-reviewer/skills/security-review-protocol. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Security Review Protocol?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Security Review Protocol?

As many as you like. As long as a model supports skills, you can use Security Review Protocol with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Security Review Protocol AI skill free?

Yes. It is published on GitHub by dianyike under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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