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Owasp Web

Community
nahid-sparktales
owasp-web

Hunt the web failure classes that actually recur — broken access control, injection, XSS by output context, SSRF, unsafe deserialization and mass assignment, session and token handling, secrets leakage, file handling — as patterns to find in this codebase. Use when writing or reviewing request handlers, queries, URL fetches, templates, uploads or auth code, or when asked to check a web app for the common vulnerability classes. Not for design-stage modeling (threat-modeling), not infrastructure or network hardening, and not a list to recite without reading code.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill nameowasp-web
Stars
58
Forks
5
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by nahid-sparktales on GitHub. Read the source before you install it.

Installation

Install the Owasp Web 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/nahid-sparktales/agent-dispatcher/tree/main/skills/security/owasp-web
  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/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/security/owasp-web .claude/skills/owasp-web
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Owasp Web 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 Web 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 Web 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.

Web failure classes

These classes recur because frameworks make the unsafe version shorter to write. Finding them is reading for a pattern — untrusted input reaching somewhere that treats it as instruction — not reciting a taxonomy. A class you name without a file and a path is not a finding.

When this fires

Code that receives requests, builds queries, renders output, fetches URLs, parses serialized data, handles sessions or accepts files is being written or reviewed. Use it inside a review pass, or on its own when asked to sweep an app for the usual suspects.

Procedure

  1. Enumerate the untrusted entry points first. Route tables and handlers, GraphQL resolvers and their nested fields, webhook receivers, queue consumers, file uploads, CLI and admin scripts, anything parsing a header or cookie. Every class below is checked from these, not by browsing files alphabetically.
  2. Broken access control, before anything else. It is the most common and the least visible to tooling. Look for a handler taking an id from the path, body or query and fetching without scoping to the caller; middleware applied by opt-in so a new route is unprotected by default; bulk and batch endpoints checking only the first item; a role or tenant id read from the request. See the authorization skill for the model itself.
  3. Injection — find where data reaches an interpreter as syntax. SQL built by concatenation or template literals, an ORM's raw escape hatch, a shell invoked with a command string rather than an argument vector, a query object built straight from a JSON body, an LDAP or XPath filter, a template rendered from a user-supplied string, eval and its relatives. The fix is parameterization or passing arguments as data — hand-rolled escaping is a defect with a delay.
  4. XSS is an output-context question. Ask where the string lands: HTML body, attribute, URL attribute, inside a script, inside CSS. Look for raw-HTML sinks (innerHTML and each framework's dangerous-HTML prop), auto-escaping turned off, user text interpolated into an inline script or a href, Markdown rendered to HTML without sanitisation, and SVG or HTML uploads served from the app's own origin. Escaping correct for one context is wrong in another.
  5. SSRF — anywhere the server fetches a URL a user influenced. Webhook targets, avatar and image fetchers, link previews, PDF and screenshot renderers, import-from-URL, identity discovery documents. Check for an allowlist of destinations, whether redirects are followed blindly, and whether the check happens before or after DNS resolution. Blocking metadata addresses by string match is not a control: alternate encodings, IPv6, redirects and rebinding all walk past it.
  6. Deserialization and object binding. Untrusted bytes reaching a language's native deserializer, a YAML loader that constructs arbitrary types, or JSON that names its own class, is remote code execution waiting for a gadget. Alongside it: mass assignment binding a whole request body onto a model (isAdmin arrives free), and merge helpers in JavaScript that let a body reach the prototype chain.
  7. Sessions and tokens. Where is a session created, rotated on privilege change, and invalidated on logout and password change? Cookie flags set? Password-reset tokens random, single-use and short-lived? For signed tokens, check that verification pins the algorithm and the key and checks expiry and audience — accepting whatever algorithm the token names is the classic hole.
  8. Secrets and inadvertent disclosure. Keys committed to the repository or baked into a client bundle by a public-prefixed environment variable, tokens in URLs and logs, stack traces and debug routes reachable in production, error messages distinguishing "no such user" from "wrong password" where that matters.
  9. File handling. Path traversal in download and upload names, archive extraction writing outside its target directory, trusting a client-declared content type, and serving user files from the application's origin where they inherit its cookies and permissions.
  10. Dependencies: audit output is leads, not findings. Run the ecosystem's audit, then check whether the vulnerable code path is actually reachable from step 1's entry points. Reporting an advisory list as a security review is how real findings get ignored.
  11. Confirm reachability before reporting. Source, sink, and a path between them that survives the checks in between. Without that path, report it as hardening and label it so.

Sending payloads at a running system is a different act from reading code. It stops and asks first, names the target, and does not happen against production or anything the user has not said they own. Reading is always in scope; probing is not implied by it.

Checklist

  • Entry points enumerated before any class was checked
  • Access control checked per handler, including bulk endpoints and new-route defaults
  • Every interpreter boundary found and checked for parameterization
  • Output sinks checked per context, including uploads served from the app origin
  • Every server-side fetch of a user-influenced URL identified
  • Deserializers, model binding and merge helpers traced back to untrusted input
  • Session lifecycle and token verification read, not assumed
  • Repository, logs, bundles and error paths checked for secrets and disclosure
  • File name, archive and content-type handling checked
  • Dependency advisories triaged for reachability
  • Each reported item has a source, a sink and a path; hardening labelled as hardening
  • Nothing was probed against a live system without asking

Failure handling

  • The pattern appears but the input turns out to be trusted — drop it or downgrade it to hardening. A false positive costs more than a missed low-severity issue: it teaches the reader to skim.
  • A framework may already neutralise it — check the framework's actual behaviour for that version rather than assuming either way, and say which behaviour you relied on.
  • The sink is reached through code you cannot follow (dynamic dispatch, generated code, a vendored blob) — report it as an unresolved path with what you traced, not as safe.
  • A class cannot be checked at all — no access to the templates, the infra config, the dependency lockfile — name it as unchecked in the report. Silence reads as a pass.
  • Something looks exploitable — that is a hypothesis until it is run by someone authorized to run it. Write it as "reachable by inspection", not "exploitable".

Evidence to report

Per finding: file and line, the untrusted input, the sink, the path between them, the concrete consequence to a named asset, and the smallest fix. Per review: which entry points were enumerated, which classes were checked, which were not checked and why, audit output with its reachability triage, and everything reasoned about but not run, said so plainly.

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 Web AI skill do?

Hunt the web failure classes that actually recur — broken access control, injection, XSS by output context, SSRF, unsafe deserialization and mass assignment, session and token handling, secrets leakage, file handling — as patterns to find in this codebase. Use when writing or reviewing request handlers, queries, URL fetches, templates, uploads or auth code, or when asked to check a web app for the common vulnerability classes. Not for design-stage modeling (threat-modeling), not infrastructure or network hardening, and not a list to recite without reading code.

Why use Owasp Web on TypingMind?

Because you install it once and use it with any model. Owasp Web 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 Web in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/security/owasp-web. 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 Web?

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 Web?

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

Is the Owasp Web AI skill free?

Yes. It is published on GitHub by nahid-sparktales 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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