Strix•JWT 身份认证测试 logo

Strix•JWT 身份认证测试

Community
asdfgh1445
Strix•JWT 身份认证测试

Strix JWT 与 OIDC 安全测试手册,覆盖令牌伪造、算法混淆与声明篡改;触发名:strix-authentication-jwt

Overview

Publisherasdfgh1445
Repositoryctf-super-hub
Skill nameStrix•JWT 身份认证测试
Stars
836
Forks
105
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by asdfgh1445 on GitHub. Read the source before you install it.

Installation

Install the Strix•JWT 身份认证测试 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/asdfgh1445/ctf-super-hub/tree/main/strix-authentication-jwt
  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/asdfgh1445/ctf-super-hub.git /tmp/ctf-super-hub
mkdir -p .claude/skills
cp -r /tmp/ctf-super-hub/strix-authentication-jwt .claude/skills/asdfgh1445-strix-jwt
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Strix•JWT 身份认证测试 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 Strix•JWT 身份认证测试 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 Strix•JWT 身份认证测试 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.

Authentication / JWT / OIDC

JWT/OIDC failures often enable token forgery, token confusion, cross-service acceptance, and durable account takeover. Do not trust headers, claims, or token opacity without strict validation bound to issuer, audience, key, and context.

Attack Surface

  • Web/mobile/API authentication using JWT (JWS/JWE) and OIDC/OAuth2
  • Access vs ID tokens, refresh tokens, device/PKCE/Backchannel flows
  • First-party and microservices verification, gateways, and JWKS distribution

Reconnaissance

Endpoints

  • Well-known: /.well-known/openid-configuration, /oauth2/.well-known/openid-configuration
  • Keys: /jwks.json, rotating key endpoints, tenant-specific JWKS
  • Auth: /authorize, /token, /introspect, /revoke, /logout, device code endpoints
  • App: /login, /callback, /refresh, /me, /session, /impersonate

Token Features

  • Headers: {"alg":"RS256","kid":"...","typ":"JWT","jku":"...","x5u":"...","jwk":{...}}
  • Claims: {"iss":"...","aud":"...","azp":"...","sub":"user","scope":"...","exp":...,"nbf":...,"iat":...}
  • Formats: JWS (signed), JWE (encrypted). Note unencoded payload option ("b64":false) and critical headers ("crit")

Key Vulnerabilities

Signature Verification

  • RS256→HS256 confusion: change alg to HS256 and use the RSA public key as HMAC secret if algorithm is not pinned
  • "none" algorithm acceptance: set "alg":"none" and drop the signature if libraries accept it
  • ECDSA malleability/misuse: weak verification settings accepting non-canonical signatures

Header Manipulation

  • kid injection: path traversal ../../../../keys/prod.key, SQL/command/template injection in key lookup, or pointing to world-readable files
  • jku/x5u abuse: host attacker-controlled JWKS/X509 chain; if not pinned/whitelisted, server fetches and trusts attacker keys
  • jwk header injection: embed attacker JWK in header; some libraries prefer inline JWK over server-configured keys
  • SSRF via remote key fetch: exploit JWKS URL fetching to reach internal hosts

Key and Cache Issues

  • JWKS caching TTL and key rollover: accept obsolete keys; race rotation windows; missing kid pinning → accept any matching kty/alg
  • Mixed environments: same secrets across dev/stage/prod; key reuse across tenants or services
  • Fallbacks: verification succeeds when kid not found by trying all keys or no keys (implementation bugs)

Claims Validation Gaps

  • iss/aud/azp not enforced: cross-service token reuse; accept tokens from any issuer or wrong audience
  • scope/roles fully trusted from token: server does not re-derive authorization; privilege inflation via claim edits when signature checks are weak
  • exp/nbf/iat not enforced or large clock skew tolerance; accept long-expired or not-yet-valid tokens
  • typ/cty not enforced: accept ID token where access token required (token confusion)

Token Confusion and OIDC

  • Access vs ID token swap: use ID token against APIs when they only verify signature but not audience/typ
  • OIDC mix-up: redirect_uri and client mix-ups causing tokens for Client A to be redeemed at Client B
  • PKCE downgrades: missing S256 requirement; accept plain or absent code_verifier
  • State/nonce weaknesses: predictable or missing → CSRF/logical interception of login
  • Device/Backchannel flows: codes and tokens accepted by unintended clients or services

Refresh and Session

  • Refresh token rotation not enforced: reuse old refresh token indefinitely; no reuse detection
  • Long-lived JWTs with no revocation: persistent access post-logout
  • Session fixation: bind new tokens to attacker-controlled session identifiers or cookies

Transport and Storage

  • Token in localStorage/sessionStorage: susceptible to XSS exfiltration; cookie vs header trade-offs with SameSite/CSRF
  • Insecure CORS: wildcard origins with credentialed requests expose tokens and protected responses
  • TLS and cookie flags: missing Secure/HttpOnly; lack of mTLS or DPoP/"cnf" binding permits replay from another device

Advanced Techniques

Microservices and Gateways

  • Audience mismatch: internal services verify signature but ignore aud → accept tokens for other services
  • Header trust: edge or gateway injects X-User-Id; backend trusts it over token claims
  • Asynchronous consumers: workers process messages with bearer tokens but skip verification on replay

JWS Edge Cases

  • Unencoded payload (b64=false) with crit header: libraries mishandle verification paths
  • Nested JWT (JWT-in-JWT) verification order errors; outer token accepted while inner claims ignored

Special Contexts

Mobile

  • Deep-link/redirect handling bugs leak codes/tokens; insecure WebView bridges exposing tokens
  • Token storage in plaintext files/SQLite/Keychain/SharedPrefs; backup/adb accessible

SSO Federation

  • Misconfigured trust between multiple IdPs/SPs, mixed metadata, or stale keys lead to acceptance of foreign tokens

Chaining Attacks

  • XSS → token theft → replay across services with weak audience checks
  • SSRF → fetch private JWKS → sign tokens accepted by internal services
  • Host header poisoning → OIDC redirect_uri poisoning → code capture
  • IDOR in sessions/impersonation endpoints → mint tokens for other users

Testing Methodology

  1. Inventory issuers/consumers - Identity providers, API gateways, services, mobile/web clients
  2. Capture tokens - Access and ID tokens for multiple roles; note header, claims, signature
  3. Map verification endpoints - /.well-known, /jwks.json
  4. Build matrix - Token Type × Audience × Service; attempt cross-use
  5. Mutate components - Headers (alg, kid, jku/x5u/jwk), claims (iss/aud/azp/sub/exp), signatures
  6. Verify enforcement - What is actually checked vs assumed

Validation

  1. Show forged or cross-context token acceptance (wrong alg, wrong audience/issuer, or attacker-signed JWKS)
  2. Demonstrate access token vs ID token confusion at an API
  3. Prove refresh token reuse without rotation detection or revocation
  4. Confirm header abuse (kid/jku/x5u/jwk) leading to key selection under attacker control
  5. Provide owner vs non-owner evidence with identical requests differing only in token context

False Positives

  • Token rejected due to strict audience/issuer enforcement
  • Key pinning with JWKS whitelist and TLS validation
  • Short-lived tokens with rotation and revocation on logout
  • ID token not accepted by APIs that require access tokens

Impact

  • Account takeover and durable session persistence
  • Privilege escalation via claim manipulation or cross-service acceptance
  • Cross-tenant or cross-application data access
  • Token minting by attacker-controlled keys or endpoints

Pro Tips

  1. Pin verification to issuer and audience; log and diff claim sets across services
  2. Attempt RS256→HS256 and "none" first only if algorithm pinning is unclear; otherwise focus on header key control (kid/jku/x5u/jwk)
  3. Test token reuse across all services; many backends only check signature, not audience/typ
  4. Exploit JWKS caching and rotation races; try retired keys and missing kid fallbacks
  5. Exercise OIDC flows with PKCE/state/nonce variants and mixed clients; look for mix-up
  6. Try DPoP/mTLS absence to replay tokens from different devices
  7. Treat refresh as its own surface: rotation, reuse detection, and audience scoping
  8. Validate every acceptance path: gateway, service, worker, WebSocket, and gRPC
  9. Favor minimal PoCs that clearly show cross-context acceptance and durable access
  10. When in doubt, assume verification differs per stack (mobile vs web vs gateway) and test each

Summary

Verification must bind the token to the correct issuer, audience, key, and client context on every acceptance path. Any missing binding enables forgery or confusion.

Frequently asked questions

What does the Strix•JWT 身份认证测试 AI skill do?

Strix JWT 与 OIDC 安全测试手册,覆盖令牌伪造、算法混淆与声明篡改;触发名:strix-authentication-jwt

Why use Strix•JWT 身份认证测试 on TypingMind?

Because you install it once and use it with any model. Strix•JWT 身份认证测试 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 Strix•JWT 身份认证测试 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/asdfgh1445/ctf-super-hub/tree/main/strix-authentication-jwt. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Strix•JWT 身份认证测试?

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 Strix•JWT 身份认证测试?

As many as you like. As long as a model supports skills, you can use Strix•JWT 身份认证测试 with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Strix•JWT 身份认证测试 AI skill free?

It is published on GitHub by asdfgh1445. Check the repository for licensing terms. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇