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Strix•SSRF 测试

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asdfgh1445
Strix•SSRF 测试

Strix SSRF 测试手册,覆盖云元数据访问、内网服务发现与协议走私;触发名:strix-ssrf

Overview

Publisherasdfgh1445
Repositoryctf-super-hub
Skill nameStrix•SSRF 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•SSRF 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-ssrf
  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-ssrf .claude/skills/asdfgh1445-strix-ssrf
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Strix•SSRF 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•SSRF 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•SSRF 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.

SSRF

Server-Side Request Forgery enables the server to reach networks and services the attacker cannot. Focus on cloud metadata endpoints, service meshes, Kubernetes, and protocol abuse to turn a single fetch into credentials, lateral movement, and sometimes RCE.

Attack Surface

Scope

  • Outbound HTTP/HTTPS fetchers (proxies, previewers, importers, webhook testers)
  • Non-HTTP protocols via URL handlers (gopher, dict, file, ftp, smb wrappers)
  • Service-to-service hops through gateways and sidecars (envoy/nginx)
  • Cloud and platform metadata endpoints, instance services, and control planes

Direct URL Params

  • url=, link=, fetch=, src=, webhook=, avatar=, image=

Indirect Sources

  • Open Graph/link previews, PDF/image renderers
  • Server-side analytics (Referer trackers), import/export jobs
  • Webhooks/callback verifiers

Protocol-Translating Services

  • PDF via wkhtmltopdf/Chrome headless, image pipelines
  • Document parsers, SSO validators, archive expanders

Less Obvious

  • GraphQL resolvers that fetch by URL
  • Background crawlers, repository/package managers (git, npm, pip)
  • Calendar (ICS) fetchers

High-Value Targets

AWS

  • IMDSv1: http://169.254.169.254/latest/meta-data/ → /iam/security-credentials/{role}, /user-data
  • IMDSv2: requires token via PUT /latest/api/token with header X-aws-ec2-metadata-token-ttl-seconds, then include X-aws-ec2-metadata-token on subsequent GETs
  • If sink cannot set headers or methods, seek intermediaries that can
  • ECS/EKS task credentials: http://169.254.170.2$AWS_CONTAINER_CREDENTIALS_RELATIVE_URI

GCP

  • Endpoint: http://metadata.google.internal/computeMetadata/v1/
  • Required header: Metadata-Flavor: Google
  • Target: /instance/service-accounts/default/token

Azure

  • Endpoint: http://169.254.169.254/metadata/instance?api-version=2021-02-01
  • Required header: Metadata: true
  • MSI OAuth: /metadata/identity/oauth2/token

Kubernetes

  • Kubelet: 10250 (authenticated) and 10255 (deprecated read-only)
  • Probe /pods, /metrics, exec/attach endpoints
  • API server: https://kubernetes.default.svc/
  • Authorization often needs service account token; SSRF that propagates headers/cookies may reuse them
  • Service discovery: attempt cluster DNS names (svc.cluster.local) and default services (kube-dns, metrics-server)

Internal Services

  • Docker API: http://localhost:2375/v1.24/containers/json (no TLS variants often internal-only)
  • Redis/Memcached: dict://localhost:11211/stat, gopher payloads to Redis on 6379
  • Elasticsearch/OpenSearch: http://localhost:9200/_cat/indices
  • Message brokers/admin UIs: RabbitMQ, Kafka REST, Celery/Flower, Jenkins crumb APIs
  • FastCGI/PHP-FPM: gopher://localhost:9000/ (craft records for file write/exec when app routes to FPM)

Key Vulnerabilities

Protocol Exploitation

Gopher

  • Speak raw text protocols (Redis/SMTP/IMAP/HTTP/FCGI)
  • Use to craft multi-line payloads, schedule cron via Redis, or build FastCGI requests

File and Wrappers

  • file:///etc/passwd, file:///proc/self/environ when libraries allow file handlers
  • jar:, netdoc:, smb:// and language-specific wrappers (php://, expect://) where enabled

Address Variants

  • Loopback: 127.0.0.1, 127.1, 2130706433, 0x7f000001, ::1, [::ffff:127.0.0.1]
  • RFC1918/link-local: 10/8, 172.16/12, 192.168/16, 169.254/16
  • Test IPv6-mapped and mixed-notation forms

URL Confusion

  • Userinfo and fragments: http://internal@attacker/ or http://attacker#@internal/
  • Scheme-less/relative forms the server might complete internally: //169.254.169.254/
  • Trailing dots and mixed case: internal. vs INTERNAL, Unicode dot lookalikes

Redirect Abuse

  • Allowlist only applied pre-redirect: 302 from attacker → internal host
  • Test multi-hop and protocol switches (http→file/gopher via custom clients)

Header and Method Control

  • Some sinks reflect or allow CRLF-injection into the request line/headers
  • If arbitrary headers/methods are possible, IMDSv2, GCP, and Azure become reachable

Bypass Techniques

Address Encoding

  • Decimal, hex, octal representations of IP addresses
  • IPv6 variants, IPv4-mapped IPv6, mixed notation

DNS Rebinding

  • First resolution returns allowed IP, second returns internal target
  • Use short TTL DNS records under attacker control

URL Parser Differentials

  • Different parsing between allowlist checker and actual fetcher
  • Exploit inconsistencies in scheme, host, port, path handling

Redirect Chains

  • Initial URL passes allowlist, redirect targets internal host
  • Protocol downgrade/upgrade through redirects

Blind SSRF

  • Use OAST (DNS/HTTP) to confirm egress
  • Derive internal reachability from timing, response size, TLS errors, and ETag differences
  • Build a port map by binary searching timeouts (short connect/read timeouts yield cleaner diffs)

Chaining Attacks

  • SSRF → Metadata creds → cloud API access (list buckets, read secrets)
  • SSRF → Redis/FCGI/Docker → file write/command execution → shell
  • SSRF → Kubelet/API → pod list/logs → token/secret discovery → lateral movement

Testing Methodology

  1. Identify surfaces - Every user-influenced URL/host/path across web/mobile/API and background jobs
  2. Establish oracle - Quiet OAST DNS/HTTP callbacks first
  3. Internal addressing - Pivot to loopback, RFC1918, link-local, IPv6, hostnames
  4. Protocol variations - Test gopher, file, dict where supported
  5. Parser differentials - Test across frameworks, CDNs, and language libraries
  6. Redirect behavior - Single-hop, multi-hop, protocol switches
  7. Header/method control - Can you influence request headers or HTTP method?
  8. High-value targets - Metadata, kubelet, Redis, FastCGI, Docker, Vault, internal admin panels

Validation

  1. Prove an outbound server-initiated request occurred (OAST interaction or internal-only response differences)
  2. Show access to non-public resources (metadata, internal admin, service ports) from the vulnerable service
  3. Where possible, demonstrate minimal-impact credential access (short-lived token) or a harmless internal data read
  4. Confirm reproducibility and document request parameters that control scheme/host/headers/method and redirect behavior

False Positives

  • Client-side fetches only (no server request)
  • Strict allowlists with DNS pinning and no redirect following
  • SSRF simulators/mocks returning canned responses without real egress
  • Blocked egress confirmed by uniform errors across all targets and protocols

Impact

  • Cloud credential disclosure with subsequent control-plane/API access
  • Access to internal control panels and data stores not exposed publicly
  • Lateral movement into Kubernetes, service meshes, and CI/CD
  • RCE via protocol abuse (FCGI, Redis), Docker daemon access, or scriptable admin interfaces

Pro Tips

  1. Prefer OAST callbacks first; then iterate on internal addressing and protocols
  2. Test IPv6 and mixed-notation addresses; filters often ignore them
  3. Observe library/client differences (curl, Java HttpClient, Node, Go); behavior changes across services and jobs
  4. Redirects are leverage: control both the initial allowlisted host and the next hop
  5. Metadata endpoints require headers/methods; verify if your sink can set them or if intermediaries add them
  6. Use tiny payloads and tight timeouts to map ports with minimal noise
  7. When responses are masked, diff length/ETag/status and TLS error classes to infer reachability
  8. Chain quickly to durable impact (short-lived tokens, harmless internal reads) and stop there

Summary

Any feature that fetches remote content on behalf of a user is a potential tunnel to internal networks and control planes. Bind scheme/host/port/headers explicitly or expect an attacker to route through them.

Frequently asked questions

What does the Strix•SSRF 测试 AI skill do?

Strix SSRF 测试手册,覆盖云元数据访问、内网服务发现与协议走私;触发名:strix-ssrf

Why use Strix•SSRF 测试 on TypingMind?

Because you install it once and use it with any model. Strix•SSRF 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•SSRF 测试 in TypingMind?

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

Which AI models can use Strix•SSRF 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•SSRF 测试?

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

Is the Strix•SSRF 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.

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