Detect Server-Side Request Forgery (SSRF) vulnerabilities in a codebase using a three-phase approach: recon (find outbound call sites), batched verify (trace user input to destinations in parallel…
Install the "sast-ssrf" agent skill from https://github.com/utkusen/sast-skills/tree/main/sast-files/.agents/skills/sast-ssrf into .claude/skills/sast-ssrf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sast-ssrf", then confirm the skill loads.
Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
skills CLI
$ npx skills add utkusen/sast-skills --skill sast-ssrf -a codex
Project install goes to .agents/skills/; add -g for ~/.codex/skills/.
Install the "sast-ssrf" agent skill from https://github.com/utkusen/sast-skills/tree/main/sast-files/.agents/skills/sast-ssrf into .agents/skills/sast-ssrf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sast-ssrf", then confirm the skill loads.
Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
skills CLI
$ npx skills add utkusen/sast-skills --skill sast-ssrf -a cursor
Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.
Install the "sast-ssrf" agent skill from https://github.com/utkusen/sast-skills/tree/main/sast-files/.agents/skills/sast-ssrf into .cursor/skills/sast-ssrf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sast-ssrf", then confirm the skill loads.
Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
skills CLI
$ npx skills add utkusen/sast-skills --skill sast-ssrf -a gemini-cli
Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.
Install the "sast-ssrf" agent skill from https://github.com/utkusen/sast-skills/tree/main/sast-files/.agents/skills/sast-ssrf into .gemini/skills/sast-ssrf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sast-ssrf", then confirm the skill loads.
Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
GitHub CLI
$ gh skill install utkusen/sast-skills sast-ssrf
Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
skills CLI
$ npx skills add utkusen/sast-skills --skill sast-ssrf -a github-copilot
Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.
Install the "sast-ssrf" agent skill from https://github.com/utkusen/sast-skills/tree/main/sast-files/.agents/skills/sast-ssrf into .github/skills/sast-ssrf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sast-ssrf", then confirm the skill loads.
GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
skills CLI
$ npx skills add utkusen/sast-skills --skill sast-ssrf -a opencode
OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
Install the "sast-ssrf" agent skill from https://github.com/utkusen/sast-skills/tree/main/sast-files/.agents/skills/sast-ssrf into .opencode/skills/sast-ssrf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sast-ssrf", then confirm the skill loads.
OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
Skill name
sast-ssrf
GitHub stars
1.3k
Token cost
~6.7k tokens
SKILL.md length
2,581 words
Files
1
Skills in repo
16
Repo updated
First seen
Licence
MIT
At a glance
Detect Server-Side Request Forgery (SSRF) vulnerabilities in a codebase using a three-phase approach: recon (find outbound call sites), batched verify (trace user input to destinations in parallel…
Works in 3 steps: Find All Outbound Network Call Sites → Verify — Trace User Input to Outbound… → Merge — Consolidate Batch Results
Asked to find SSRF
SKILL.md covers What is SSRF, Vulnerable vs. Secure Examples, Execution and Important Reminders
Reaches api.thirdparty.com
What it does
Sast Ssrf is an agent skill from utkusen/sast-skills. Detect Server-Side Request Forgery (SSRF) vulnerabilities in a codebase using a three-phase approach: recon (find outbound call sites), batched verify (trace user input to destinations in parallel subagents, 3 sites each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/ssrf-results.md. Use when asked to find SSRF or server-side request forgery bugs.
Its SKILL.md is about 6.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Security, covering Web application vulnerabilities, Static analysis and SAST and Backend development. The repository describes itself as: Collection of agent skills to find vulnerabilities inside your web/mobile apps. The licence is MIT.
When your agent uses it
Asked to find SSRF
Server-side request forgery bugs
Example prompts
“/sast-ssrf”
Requirements
Python 3
Node.js
Workflow steps
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit db52227. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python, javascript, php, markdown, ruby, java, go and csharp).
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
api.thirdparty.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Sast Ssrf loads about 6.7k tokens when it runs. Until then it costs about 109 tokens; SKILL.md has 2,581 words of instructions outside code blocks.
Always· name and description, kept in context so the agent knows when to use it
~109
When it runs· the whole SKILL.md, loaded when a task matches
~6.7k
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
Download SKILL.mdSave it as .claude/skills/sast-ssrf/SKILL.md (or your agent's skills folder).
name
sast-ssrf
description
Detect Server-Side Request Forgery (SSRF) vulnerabilities in a codebase using a three-phase approach: recon (find outbound call sites), batched verify (trace user input to destinations in parallel subagents, 3 sites each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/ssrf-results.md. Use when asked to find SSRF or server-side request forgery bugs.
Server-Side Request Forgery (SSRF) Detection
You are performing a focused security assessment to find SSRF vulnerabilities in a codebase. This skill uses a three-phase approach with subagents: recon (find all places that make outbound TCP, DNS, or HTTP requests), batched verify (trace whether user-supplied input reaches those call sites, in parallel batches of 3), and merge (consolidate batch reports into one file).
Prerequisites: sast/architecture.md must exist. Run the analysis skill first if it doesn't.
What is SSRF
SSRF occurs when an attacker can cause the server to make outbound network requests to an arbitrary destination — including internal services, cloud metadata endpoints, or other external targets — by supplying or influencing the URL, hostname, IP, or port used in a server-side request.
The core pattern: unvalidated, user-controlled input reaches the destination argument of an outbound network call.
What SSRF IS
HTTP client calls where the URL or host is built from user input: requests.get(user_url)
Fetching a resource whose location is provided by the client: fetch(req.body.webhook_url)
DNS lookups on a hostname supplied by the user: dns.lookup(req.query.host)
Raw TCP connections to a host/port derived from user input: socket.connect((user_host, user_port))
File-fetching functions used with HTTP/FTP URLs from user input: file_get_contents($user_url)
URL redirectors that forward to a user-supplied destination without validation
Webhooks, import-from-URL, screenshot services, PDF renderers, image proxies — any feature that fetches a remote resource on behalf of the user
What SSRF is NOT
Do not flag these:
Open redirects: Redirecting the browser (HTTP 302) to a user-supplied URL — that's a client-side redirect, not a server-side request
XSS via URL: Rendering a user-supplied URL in an <a> tag without escaping — that's XSS
IDOR: Accessing another user's data by changing an object ID — separate vulnerability class
Hardcoded outbound calls: HTTP requests to fixed, fully hardcoded URLs with no user influence — not SSRF
Patterns That Prevent SSRF
When you see these patterns, the code is likely not vulnerable:
1. Strict allowlist of permitted destinations
python
ALLOWED_HOSTS = {"api.example.com", "cdn.example.com"}
parsed = urlparse(user_url)
if parsed.hostname not in ALLOWED_HOSTS:
raise ValueError("Destination not allowed")
requests.get(user_url)
2. Allowlist of permitted URL prefixes / schemes
python
ALLOWED_PREFIXES = ["https://api.example.com/", "https://cdn.example.com/"]
if not any(user_url.startswith(p) for p in ALLOWED_PREFIXES):
abort(400)
requests.get(user_url)
3. No user influence on the destination
python
# Destination fully hardcoded — no user input involved
response = requests.get("https://api.thirdparty.com/data")
Note: IP blocklists (blocking 169.254.0.0/16, 10.0.0.0/8, etc.) are not sufficient protection — they can be bypassed via DNS rebinding, URL encoding, IPv6 notation, decimal IP representation, or redirect chains. Do not treat a blocklist as making a site safe; classify it as Likely Vulnerable.
Vulnerable vs. Secure Examples
Python — requests
python
# VULNERABLE: URL fully controlled by user
@app.route('/fetch')
def fetch():
url = request.args.get('url')
response = requests.get(url)
return response.text
# SECURE: strict allowlist on destination host
ALLOWED = {"api.example.com"}
@app.route('/fetch')
def fetch():
url = request.args.get('url')
if urlparse(url).hostname not in ALLOWED:
abort(403)
response = requests.get(url)
return response.text
Python — urllib
python
# VULNERABLE: user controls the URL passed to urlopen
def preview(request):
target = request.GET.get('target')
data = urllib.request.urlopen(target).read()
return HttpResponse(data)
# SECURE: only allow https scheme to a hardcoded host
def preview(request):
target = request.GET.get('target')
parsed = urlparse(target)
if parsed.scheme != 'https' or parsed.hostname != 'media.example.com':
return HttpResponse(status=400)
data = urllib.request.urlopen(target).read()
return HttpResponse(data)
Node.js — fetch / axios
javascript
// VULNERABLE: webhook URL comes directly from request body
app.post('/webhook/test', async (req, res) => {
const { url } = req.body;
const result = await fetch(url);
res.json(await result.json());
});
// SECURE: allowlist check before fetch
const ALLOWED_HOSTS = new Set(['hooks.example.com']);
app.post('/webhook/test', async (req, res) => {
const { url } = req.body;
const { hostname } = new URL(url);
if (!ALLOWED_HOSTS.has(hostname)) return res.status(403).send('Forbidden');
const result = await fetch(url);
res.json(await result.json());
});
# VULNERABLE: open() fetches arbitrary URL
def import
url = params[:url]
content = URI.open(url).read # also triggers for open(url) via Kernel#open
# ...
end
# SECURE: restrict scheme and host
def import
url = params[:url]
uri = URI.parse(url)
raise "Forbidden" unless uri.is_a?(URI::HTTPS) && uri.host == "data.example.com"
content = uri.open.read
# ...
end
PHP — cURL
php
// VULNERABLE: user-supplied URL piped into curl
function fetch_preview($url) {
$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, $url);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$result = curl_exec($ch);
curl_close($ch);
return $result;
}
// Called as: fetch_preview($_GET['url'])
// SECURE: validate URL against allowlist before curl
function fetch_preview($url) {
$allowed = ['https://cdn.example.com/'];
foreach ($allowed as $prefix) {
if (strpos($url, $prefix) === 0) {
// ... proceed with curl
}
}
throw new Exception("Destination not allowed");
}
PHP — file_get_contents
php
// VULNERABLE: file_get_contents with http:// wrapper and user input
$url = $_GET['source'];
$data = file_get_contents($url); // fetches remote URL if scheme is http/https/ftp
Java — Spring / OkHttp
java
// VULNERABLE: RestTemplate with user-controlled URL
@GetMapping("/proxy")
public ResponseEntity<String> proxy(@RequestParam String url) {
RestTemplate restTemplate = new RestTemplate();
return restTemplate.getForEntity(url, String.class);
}
// VULNERABLE: OkHttp with user-controlled host
public String fetch(String host, String path) {
Request request = new Request.Builder()
.url("https://" + host + path)
.build();
return client.newCall(request).execute().body().string();
}
Go — net/http
go
// VULNERABLE: user-supplied URL passed to http.Get
func proxyHandler(w http.ResponseWriter, r *http.Request) {
target := r.URL.Query().Get("url")
resp, err := http.Get(target)
if err != nil {
http.Error(w, err.Error(), 500)
return
}
io.Copy(w, resp.Body)
}
// VULNERABLE: user controls host in net.Dial
func dialHandler(w http.ResponseWriter, r *http.Request) {
host := r.URL.Query().Get("host")
port := r.URL.Query().Get("port")
conn, _ := net.Dial("tcp", host+":"+port)
// ...
}
C# — HttpClient
csharp
// VULNERABLE: user-supplied URL passed to HttpClient
[HttpGet("proxy")]
public async Task<IActionResult> Proxy([FromQuery] string url)
{
var response = await _httpClient.GetAsync(url);
var content = await response.Content.ReadAsStringAsync();
return Content(content);
}
Execution
This skill runs in three phases using subagents. Pass the contents of sast/architecture.md to all subagents as context.
Phase 1: Find All Outbound Network Call Sites
Launch a subagent with the following instructions:
Goal: Find every location in the codebase where the application makes an outbound network request — HTTP, HTTPS, FTP, TCP, or DNS — regardless of whether that destination is user-controlled. Write results to sast/ssrf-recon.md.
Context: You will be given the project's architecture summary. Use it to understand the tech stack, HTTP client libraries in use, and any networking or webhook-related components.
What to search for — outbound request call sites:
You are looking for any code that opens a network connection or fetches a remote resource. Flag ANY call where a non-trivially-hardcoded URL, host, or address value is passed as an argument. You are not yet tracing whether that value is user-controlled; that is Phase 2's job.
Any curl, wget, nc, ncat, nmap invocation where the target is a variable
What to skip (these are safe — do not flag):
Calls where the entire URL and hostname are fully hardcoded string literals with no dynamic parts: requests.get("https://api.example.com/data")
Internal loopback connections to localhost or 127.0.0.1 that are clearly part of service-to-service architecture (e.g., connecting to a local queue) — flag these if the address is dynamic
Output format — write to sast/ssrf-recon.md:
markdown
# SSRF Recon: [Project Name]
## Summary
Found [N] outbound network call sites.
## Outbound Call Sites
### 1. [Descriptive name — e.g., "HTTP GET in webhook dispatcher"]
- **File**: `path/to/file.ext` (lines X-Y)
- **Function / endpoint**: [function name or route]
- **Call type**: [HTTP GET / HTTP POST / TCP dial / DNS lookup / subprocess curl / etc.]
- **Library / method**: [requests.get / fetch / http.Get / curl_exec / etc.]
- **Destination argument**: `var_name` or `url_expression` — [brief note, e.g., "assembled from query param" or "partially hardcoded path with variable host"]
- **Code snippet**:
[the outbound call and the lines immediately before it that construct the destination]
[Repeat for each site]
After Phase 1: Check for Candidates Before Proceeding
After Phase 1 completes, read sast/ssrf-recon.md. If the recon found zero outbound call sites (the summary reports "Found 0" or the "Outbound Call Sites" section is empty or absent), skip Phase 2 and Phase 3 entirely. Instead, write the following content to sast/ssrf-results.md and stop:
markdown
# SSRF Analysis Results
No vulnerabilities found.
Only proceed to Phase 2 if Phase 1 found at least one outbound call site.
Phase 2: Verify — Trace User Input to Outbound Call Sites (Batched)
After Phase 1 completes, read sast/ssrf-recon.md and split the outbound call sites into batches of up to 3 sites each. Launch one subagent per batch in parallel. Each subagent traces taint only for its assigned sites and writes results to its own batch file.
Batching procedure (you, the orchestrator, do this — not a subagent):
Read sast/ssrf-recon.md and count the numbered site sections (### 1., ### 2., etc.) under "Outbound Call Sites".
Divide them into batches of up to 3. For example, 8 sites → 3 batches (1-3, 4-6, 7-8).
For each batch, extract the full text of those site sections from the recon file.
Launch all batch subagents in parallel, passing each one only its assigned sites.
Each subagent writes to sast/ssrf-batch-N.md where N is the 1-based batch number.
Identify the project's primary language/framework from sast/architecture.md and select only the matching examples from the "Vulnerable vs. Secure Examples" section above. For example, if the project uses Node.js with fetch/axios, include only the "Node.js — fetch / axios" and "Node.js — http.request" examples. Include these selected examples in each subagent's instructions where indicated by [TECH-STACK EXAMPLES] below.
Give each batch subagent the following instructions (substitute the batch-specific values):
Goal: For each assigned outbound network call site, determine whether a user-supplied value controls or influences the destination (URL, host, path, port, or scheme). Our goal is to find SSRF vulnerabilities. Write results to sast/ssrf-batch-[N].md.
Your assigned outbound call sites (from the recon phase):
[Paste the full text of the assigned site sections here, preserving the original numbering]
Context: You will be given the project's architecture summary. Use it to understand entry points, middleware, and how data flows through the application.
SSRF reference — what to look for:
SSRF occurs when user-controlled input reaches the destination argument of a server-side outbound network call without an effective allowlist on where the server may connect.
What SSRF is NOT — do not flag these as SSRF:
Open redirects: HTTP 302 to a user URL — client-side redirect, not a server-side request
XSS via URL: User URL rendered in HTML without escaping — XSS
IDOR: Object ID tampering — separate class
Fully hardcoded outbound URLs with no user influence — not SSRF
For each outbound call site, trace the destination argument(s) backwards to their origin:
Direct user input — the destination is assigned directly from a request source with no transformation:
Indirect / assembled destination — the URL is built by concatenating a hardcoded prefix with a user-supplied suffix or path:
"https://example.com/" + user_path — may still be exploitable via path traversal or scheme injection depending on the HTTP client
base_url + user_query — user controls the query string, potentially injectable
Flag these as Likely Vulnerable and note which portion is user-controlled
User input stored and later fetched — the destination was previously saved from user input (e.g., a stored webhook URL) and is now retrieved from the database to make a request:
Find where the stored value was written — was it accepted from user input without allowlist validation at write time?
Was any validation applied at read time before the request?
Server-side / hardcoded value — the destination comes from config, an environment variable, a hardcoded constant, or server-side logic with no user influence — this site is NOT exploitable.
For each call site, also check for mitigations:
Strict allowlist of hosts/prefixes: A hardcoded set of permitted hostnames or URL prefixes that the destination is validated against before the request is made — this is an effective mitigation. Mark as Not Vulnerable.
Scheme-only restriction (e.g., only allow https://): Partial mitigation — reduces impact but does not prevent SSRF to arbitrary HTTPS hosts. Still flag as Likely Vulnerable.
Blocklist of private IP ranges / metadata endpoints: 169.254.169.254, 10.0.0.0/8, 192.168.0.0/16, etc. — not sufficient. Bypassable via DNS rebinding, alternate IP representations, and redirect chains. Flag as Likely Vulnerable.
DNS resolution + IP check (resolve hostname first, then check resolved IP against blocklist): Stronger than a pure blocklist, but still susceptible to DNS rebinding between the check and the request (TOCTOU). Flag as Likely Vulnerable unless the same resolved IP is explicitly pinned for the request.
Vulnerable vs. secure examples for this project's tech stack:
[TECH-STACK EXAMPLES]
Classification:
Vulnerable: User input demonstrably reaches the outbound request destination with no effective mitigation (no allowlist or only a blocklist/scheme check).
Likely Vulnerable: User input probably reaches the destination (indirect flow or partial construction), or only weak mitigation is present (blocklist, scheme-only check, partial URL prefix).
Not Vulnerable: The destination is fully server-side, OR a strict host/prefix allowlist is enforced before the request.
Needs Manual Review: Cannot determine the destination's origin with confidence (opaque helpers, complex conditional flows, or external libraries that resolve the URL).
Output format — write to sast/ssrf-batch-[N].md:
markdown
# SSRF Batch [N] Results
## Findings
### [VULNERABLE] Descriptive name
- **File**: `path/to/file.ext` (lines X-Y)
- **Endpoint / function**: [route or function name]
- **Issue**: [e.g., "HTTP query param `url` flows directly into requests.get()"]
- **Taint trace**: [Step-by-step from entry point to the call site — e.g., "request.args.get('url') → target_url → requests.get(target_url)"]
- **Impact**: [What an attacker can do — access cloud metadata at 169.254.169.254, pivot to internal services, port scan the internal network, exfiltrate data, bypass firewalls, etc.]
- **Mitigation present**: [None / Blocklist only / Scheme check only — explain why it's insufficient]
- **Remediation**: [Strict host allowlist, or remove user control over destination entirely]
- **Dynamic Test**:
### [LIKELY VULNERABLE] Descriptive name
- **File**: `path/to/file.ext` (lines X-Y)
- **Endpoint / function**: [route or function name]
- **Issue**: [e.g., "User controls the path portion of a partially hardcoded URL" or "Stored webhook URL accepted without allowlist at write time"]
- **Taint trace**: [Best-effort trace with the uncertain or partial-control step identified]
- **Concern**: [Why it's still a risk — e.g., "Attacker may be able to redirect to an internal host via path traversal" or "Blocklist is bypassable via DNS rebinding"]
- **Remediation**: [Strict allowlist or remove user control]
- **Dynamic Test**:
[payload to attempt — e.g., path traversal or DNS rebinding scenario]
### [NOT VULNERABLE] Descriptive name
- **File**: `path/to/file.ext` (lines X-Y)
- **Endpoint / function**: [route or function name]
- **Reason**: [e.g., "URL is fully hardcoded" or "Strict host allowlist enforced before request"]
### [NEEDS MANUAL REVIEW] Descriptive name
- **File**: `path/to/file.ext` (lines X-Y)
- **Endpoint / function**: [route or function name]
- **Uncertainty**: [Why the destination's origin could not be determined]
- **Suggestion**: [What to trace manually — e.g., "Follow `resolve_target()` in helpers.py to check where the URL originates"]
Show full SKILL.md (498 more words)Show less
Phase 3: Merge — Consolidate Batch Results
After all Phase 2 batch subagents complete, read every sast/ssrf-batch-*.md file and merge them into a single sast/ssrf-results.md. You (the orchestrator) do this directly — no subagent needed.
Merge procedure:
Read all sast/ssrf-batch-1.md, sast/ssrf-batch-2.md, ... files.
Collect all findings from each batch file and combine them into one list, preserving the original classification and all detail fields.
Count totals across all batches for the executive summary (total sites analyzed equals the number from recon / sum of assigned sites).
Write the merged report to sast/ssrf-results.md using this format:
markdown
# SSRF Analysis Results: [Project Name]
## Executive Summary
- Outbound call sites analyzed: [total across all batches]
- Vulnerable: [N]
- Likely Vulnerable: [N]
- Not Vulnerable: [N]
- Needs Manual Review: [N]
## Findings
[All findings from all batches, grouped by classification:
VULNERABLE first, then LIKELY VULNERABLE, then NEEDS MANUAL REVIEW, then NOT VULNERABLE.
Preserve every field from the batch results exactly as written.]
After writing sast/ssrf-results.md, delete all intermediate batch files (sast/ssrf-batch-*.md).
Important Reminders
Read sast/architecture.md and pass its content to all subagents as context.
Phase 2 must run AFTER Phase 1 completes — it depends on the recon output.
Phase 3 must run AFTER all Phase 2 batches complete — it depends on all batch outputs.
Batch size is 3 outbound call sites per subagent. If there are 1-3 sites total, use a single subagent. If there are 10, use 4 subagents (3+3+3+1).
Launch all batch subagents in parallel — do not run them sequentially.
Each batch subagent receives only its assigned sites' text from the recon file, not the entire recon file. This keeps each subagent's context small and focused.
Phase 1 is purely structural: flag any call site where the destination argument is dynamic (a variable, expression, or assembled string), regardless of whether user input flows there. Do not attempt to trace user input in Phase 1 — that is Phase 2's job.
Phase 2 is purely taint analysis: for each site in its batch, trace the destination argument back to its origin. If it comes from a user-controlled source without an effective allowlist, the site is a real vulnerability.
Blocklists are not mitigations: IP blocklists for private ranges and cloud metadata endpoints are easily bypassed. Always classify such sites as Vulnerable or Likely Vulnerable, not as safe.
Partial URL control is still dangerous: even if the attacker only controls the path or query string portion of the URL, flag it as Likely Vulnerable — depending on the HTTP client behavior, redirect following, and target service, partial control can be enough.
Stored destinations are tainted: if a URL or hostname was accepted from user input at write time and is later used for an outbound request, trace the write-time acceptance. Lack of allowlist validation at write time makes it SSRF.
Subprocess curl/wget is SSRF too: shell-outs that run curl or wget with a user-supplied URL are just as dangerous as HTTP client calls. Check for these, especially in image-processing, import, or download features.
When in doubt, classify as "Needs Manual Review" rather than "Not Vulnerable". False negatives are worse than false positives in security assessment.
DNS rebinding note: for findings where only a DNS-resolution-then-blocklist check is present, note the TOCTOU window explicitly in the finding — this is a known bypass technique.
Clean up intermediate files: delete sast/ssrf-recon.md and all sast/ssrf-batch-*.md files after the final sast/ssrf-results.md is written.
Sast Ssrf next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
Run the full Spring Boot verification loop — Maven or Gradle build, SpotBugs, PMD, and Checkstyle static analysis, unit and Testcontainers integration tests with JaCoCo coverage, OWASP dependency…
Scans code with a bundled Node script for injection, secrets, XSS and other risky patterns, ranks findings by severity and checks that security decisions are documented.
Detect GraphQL injection vulnerabilities in a codebase using a three-phase approach: recon (confirm GraphQL usage and find unsafe operation document assembly sites), batched verify (trace user input…
Detect Insecure Direct Object Reference (IDOR) vulnerabilities in a codebase using a three-phase approach: recon (find candidates), batched verify (check authorization in parallel subagents, 3…
Detect business logic vulnerabilities in a codebase using a three-phase approach: threat modeling (domain analysis and attack scenarios), batched verify (check exploitable gaps in parallel…
Detect insecure file upload vulnerabilities in a codebase using a three-phase approach: discovery (find all upload sites), batched verify (check extension bypass and related issues in parallel…
Detect Server-Side Request Forgery (SSRF) vulnerabilities in a codebase using a three-phase approach: recon (find outbound call sites), batched verify (trace user input to destinations in parallel…. Sast Ssrf is an agent skill from utkusen/sast-skills. Detect Server-Side Request Forgery (SSRF) vulnerabilities in a codebase using a three-phase approach: recon (find outbound call sites), batched verify (trace user input to destinations in parallel subagents, 3 sites each), and merge (consolidate batch results).
Run `npx skills add utkusen/sast-skills --skill sast-ssrf -a claude-code`. Or copy the skill folder (sast-files/.agents/skills/sast-ssrf in utkusen/sast-skills) into .claude/skills/sast-ssrf in your project. Claude Code loads it when a task matches its description.
How do I install Sast Ssrf in Codex?
Run `npx skills add utkusen/sast-skills --skill sast-ssrf -a codex`. Or copy the skill folder (sast-files/.agents/skills/sast-ssrf in utkusen/sast-skills) into .agents/skills/sast-ssrf in your project. Codex loads it when a task matches its description.
Can I use Sast Ssrf in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add utkusen/sast-skills --skill sast-ssrf -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sast-ssrf, .gemini/skills/sast-ssrf, .github/skills/sast-ssrf and .opencode/skills/sast-ssrf in your project.
What does Sast Ssrf need to run?
SKILL.md names no scripts, command-line tools or credentials: Sast Ssrf is instructions for the agent only. Our summary lists: Python 3; Node.js.
Does Sast Ssrf access the network?
SKILL.md names 1 domain. In commands or code: api.thirdparty.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is Sast Ssrf safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Sast Ssrf use?
Sast Ssrf is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Sast Ssrf use?
About 6.7k tokens (SKILL.md is roughly 27k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Sast Ssrf?
Skills that share tags, products or a category with Sast Ssrf: Psalm Security Analysis (cachethq/core, 230 stars), Springboot Verification (affaan-m/ECC, 276k stars), Security Verification Gate (fengshao1227/ccg-workflow, 5.9k stars) and Secknowledge Skill (Pa55w0rd/secknowledge-skill, 424 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sast Ssrf?
utkusen (a GitHub user) maintains it in utkusen/sast-skills, which has 1,329 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on April 8, 2026.
Source: utkusen/sast-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.