Owasp Security Code Review logo

Owasp Security Code Review

Organization
SpecterOps
owasp-security-code-review

Perform evidence-driven security code reviews across application repositories, services, libraries, and pull requests. Use when Codex needs to audit source code for security flaws, map trust boundaries, verify authentication or authorization, trace untrusted data to sensitive sinks, assess business logic or cryptography, or produce prioritized findings when no narrower language or platform review skill fits.

Overview

PublisherSpecterOps
Repositoryskills
Skill nameowasp-security-code-review
Stars
706
Forks
80
Bundled files
16
LicenseApache-2.0
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.

  • 16 bundled files

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

  • Open source

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

Installation

Install the Owasp Security Code Review 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/SpecterOps/skills/tree/main/skills/owasp-security-code-review
  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/SpecterOps/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/owasp-security-code-review .claude/skills/owasp-security-code-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Owasp Security Code Review 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 Owasp Security Code Review 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 Owasp Security Code Review 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.

OWASP Security Code Review

Use this skill to perform a manual security review that starts from architecture and follows concrete execution paths. Prefer a narrower language, framework, CI, cloud, or infrastructure review skill when one clearly fits; use this skill for baseline coverage and cross-cutting review logic.

Review Principles

  • Build a threat model before reporting issues: identify actors, assets, trust boundaries, privilege levels, and attacker-controlled inputs.
  • Treat entry points, identity decisions, privilege changes, and sensitive data movement as primary anchors.
  • Confirm issues end to end from source through transformations and guards to sink or security decision.
  • Separate confirmed vulnerabilities from suspicious patterns and unanswered questions.
  • Prefer a small number of well-supported findings over speculative issue lists.
  • Keep coverage visible: record reviewed surfaces, skipped areas, and assumptions that affect confidence.
  • Always create or update one standalone poc_<finding_slug>.py artifact per confirmed finding in the review workspace.

References

Review Process

  1. Review architecture for security anti-patterns.

    • Inventory components, languages, frameworks, storage, external integrations, privileged jobs, and deployment boundaries.
    • Identify trust assumptions such as internal-network trust, shared admin paths, tenant co-mingling, unsafe plugin or deserialization surfaces, dynamic code execution, and secret-bearing services.
    • Note where security controls are centralized and where alternate paths may bypass them.
  2. Analyze entry points and input validation.

    • Enumerate HTTP/RPC routes, GraphQL resolvers, message consumers, webhooks, CLI commands, scheduled jobs, file imports, deserializers, and configuration inputs.
    • Trace parsing, normalization, canonicalization, schema checks, type checks, size limits, allowlists, and rejection behavior.
    • Look for alternate encodings, duplicate parameters, path confusion, object binding issues, and validation performed after a dangerous sink.
  3. Verify authentication and authorization.

    • Map how identities are established, refreshed, propagated, and revoked across user, service, and background-job flows.
    • Check session, token, API key, mTLS, and service-account validation assumptions.
    • Verify every read, write, export, and state transition enforces the required role, tenant, ownership, and object-level checks, including alternate routes and asynchronous handlers.
    • Test fail-open behavior when middleware, policy engines, or upstream identity data is absent or malformed.
  4. Trace data flows.

    • Follow untrusted data to SQL/NoSQL queries, templates, filesystem paths, archives, redirects, outbound requests, command execution, logs, serialization, caches, and client responses.
    • Follow sensitive data such as credentials, tokens, personal data, and keys through storage, telemetry, errors, exports, and third-party boundaries.
    • Record sanitizers, encoders, escaping, parameterization, and privilege boundaries on each path; verify that each control is appropriate for the sink.
  5. Analyze business logic.

    • Model critical workflows as states and invariants: approvals, payments, invitations, password resets, account recovery, quotas, entitlements, tenant isolation, and admin actions.
    • Check replay, race, ordering, stale-state, double-spend, partial-failure, and TOCTOU behavior.
    • Look for ways to skip steps, reuse artifacts, change identifiers, or invoke a privileged transition through an unexpected channel.
  6. Review cryptographic implementation.

    • Identify password hashing, encryption, signatures, MACs, randomness, key derivation, key storage, certificate validation, and token construction.
    • Verify modern primitives, secure parameters, nonce/IV uniqueness, constant-time comparisons where relevant, key separation, rotation, and failure behavior.
    • Treat custom crypto, hardcoded keys, weak randomness, disabled TLS verification, and unsigned or partially verified tokens as priority review areas.
  7. Verify error handling.

    • Check whether errors fail closed at authentication, authorization, validation, and transaction boundaries.
    • Review exception swallowing, fallback behavior, retries, partial commits, default values, verbose responses, stack traces, and secret-bearing logs.
    • Confirm that security-relevant failures are observable without leaking sensitive details.
  8. Review configuration and deployment.

    • Inspect defaults and environment parsing for debug modes, CORS, trusted proxies, host validation, cookie flags, security headers, logging, feature flags, and secret loading.
    • Review container/runtime privileges, filesystem permissions, network exposure, cloud/IAM bindings, CI/CD secrets, build-time substitutions, and production-vs-development drift when those artifacts are in scope.
    • Identify insecure defaults that make a secure deployment depend on undocumented operator behavior.
  9. Build the PoC artifacts.

    • Create or update one standalone poc_<finding_slug>.py file for each confirmed finding.
    • Keep each PoC incremental: print or implement numbered steps for prerequisites, authentication or material acquisition, trigger, impact verification, and cleanup guidance.
    • State attacker position, required permissions, credentials or certificates, environmental dependencies, and any unproven prerequisite before sending requests.
    • Default to dry-run or harmless markers and require an explicit flag for state-changing validation.
    • If a finding has no safe runnable path, still create its per-finding PoC scaffold and explain the missing prerequisite or unsafe step.
    • Validate each script with syntax checks and dry runs, then record which live steps were and were not executed.

Finding Standard

Report a finding only when the review can state:

  • the affected code path with file and line references
  • the attacker-controlled input or violated trust assumption
  • the missing, bypassed, or incorrect control
  • the reachable impact and required prerequisites
  • a concrete remediation direction
  • a focused regression test or validation step

If a concern lacks a complete path or depends on missing runtime context, label it as an open question or coverage gap instead of a confirmed vulnerability.

Output

Lead with findings ordered by severity. For each finding, include Severity, Location, Issue, Exploit Path, Impact, Remediation, Test, and PoC Requirements.

For each confirmed finding, reference the corresponding poc_<finding_slug>.py artifact in the report and include the minimum attacker position, required permissions or credentials, environmental conditions, safe default behavior, and example invocation.

After findings, include Open Questions / Assumptions and Coverage. If no confirmed findings exist, say so explicitly and still state the reviewed surfaces, unresolved risks, and test gaps.

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 Owasp Security Code Review AI skill do?

Perform evidence-driven security code reviews across application repositories, services, libraries, and pull requests. Use when Codex needs to audit source code for security flaws, map trust boundaries, verify authentication or authorization, trace untrusted data to sensitive sinks, assess business logic or cryptography, or produce prioritized findings when no narrower language or platform review skill fits.

Why use Owasp Security Code Review on TypingMind?

Because you install it once and use it with any model. Owasp Security Code Review 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 Owasp Security Code Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SpecterOps/skills/tree/main/skills/owasp-security-code-review. 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 Owasp Security Code Review?

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 Owasp Security Code Review?

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

Is the Owasp Security Code Review AI skill free?

Yes. It is published on GitHub by SpecterOps under the Apache-2.0 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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