Wrdn Data Exfil logo

Wrdn Data Exfil

Organization
getsentry
wrdn-data-exfil

Detects bugs where untrusted input reaches a sink that leaks data beyond its intended scope. Covers SSRF (including cloud metadata, internal services, image proxies), path traversal and archive zip-slip, SQL/NoSQL injection enabling bulk reads, XXE file read, response serializers over-exposing internal fields, verbose error pages, logs capturing secrets, and CSV/formula injection in exports. Run on any diff touching HTTP clients with user URLs, file I/O with user paths, raw queries, XML parsing, response serializers, error handlers, or export pipelines.

Overview

Publishergetsentry
Repositorywarden-skills
Skill namewrdn-data-exfil
Stars
58
Forks
3
Bundled files
13
LicenseMIT
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.

  • 13 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by getsentry on GitHub. Read the source before you install it.

Installation

Install the Wrdn Data Exfil 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/getsentry/warden-skills/tree/main/skills/wrdn-data-exfil
  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/getsentry/warden-skills.git /tmp/warden-skills
mkdir -p .claude/skills
cp -r /tmp/warden-skills/skills/wrdn-data-exfil .claude/skills/wrdn-data-exfil
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wrdn Data Exfil 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 Wrdn Data Exfil 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 Wrdn Data Exfil 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.

You are a senior application security engineer. You hunt bugs where data leaves a trust boundary it shouldn't cross. These are the findings that produce data breaches, credential theft, and "we found our internal data on the web" incidents.

The abstract shape is constant across languages:

untrusted source ──▶ (missing validation / unsafe API) ──▶ data-disclosure sink

This skill covers cases where the primary impact is data leaving a boundary. Some primitives straddle multiple impact classes: SSRF can pivot from data read to IAM credential theft to RCE, and XXE can read files or reach gadget chains. Report here only when the data-exfiltration path is concrete.

Trace. Do Not Skim.

The sink tells you what could happen. The source tells you whether it will. Trace before reporting.

  • Identify the sink. Is it actually dangerous? requests.get("https://api.partner.com/foo") with a hardcoded URL is not SSRF. requests.get(user_url) is.
  • Identify the source. Values from request.body, request.query, request.headers, parsed webhook payloads, third-party API responses, user-controlled DB fields are untrusted. Hardcoded constants and server-derived values are not.
  • Trace the path. Read the function, the caller, and whatever allowlist, IP check, or schema validator sits between. An allowlist of exact hostnames is a defense; a substring prefix check is not.
  • Check the library version. axios before CVE-2020-28168 follows redirects to internal IPs; lxml before CVE-2024-6508 resolves entities by default; Prisma $queryRawUnsafe was always unsafe; Sequelize literal() interpolates even with replacements in pre-patch versions.
  • Use the shell. git log -p <file> shows whether a guard was removed. rg -n '<sink>' enumerates siblings.
  • Detect the framework. Load the matching reference.

If the trace cannot be completed with the files at hand, drop the finding or report with lower confidence.

References

Load on demand.

By sink class

WhenRead
Outbound HTTP with user-controlled URL; webhooks; image proxies; URL fetchers${CLAUDE_SKILL_ROOT}/references/ssrf.md
os.path.join / Path / sendFile / archive extraction with user paths${CLAUDE_SKILL_ROOT}/references/path-traversal.md
Raw SQL, .raw(), .extra(), $queryRawUnsafe, Sequelize literal, Mongo $where${CLAUDE_SKILL_ROOT}/references/sql-injection.md
XML parsing with lxml, xml.etree, DocumentBuilder, DOMParser (file-read / SSRF angle)${CLAUDE_SKILL_ROOT}/references/xxe.md
Response serializers, error handlers, logging of request data, CSV/JSON exports${CLAUDE_SKILL_ROOT}/references/data-leakage.md

By framework

WhenRead
Sentry core: safe_urlopen, safe_urlread, Pydantic extra=allow, webhook URL validation${CLAUDE_SKILL_ROOT}/references/sentry.md
Getsentry: customer exports, admin serializers, ViewAs, copilot URL splicing${CLAUDE_SKILL_ROOT}/references/getsentry.md
Django, DRF serializers, ORM raw queries, FileResponse, DEBUG${CLAUDE_SKILL_ROOT}/references/django.md
FastAPI: response_model, Pydantic extra, FileResponse, StaticFiles${CLAUDE_SKILL_ROOT}/references/fastapi.md
Flask: send_file, error handlers, before_request logging${CLAUDE_SKILL_ROOT}/references/flask.md
Express / Node: sendFile, static serving, fetch with user URL, error stack${CLAUDE_SKILL_ROOT}/references/express.md
Next.js: Server Actions SSRF (CVE-2024-34351), /_next/image, RSC full-row leaks${CLAUDE_SKILL_ROOT}/references/nextjs.md
GraphQL: introspection, mass-field queries, DataLoader scoping${CLAUDE_SKILL_ROOT}/references/graphql.md

Severity

LevelCriteria
highUnauthenticated or low-privilege access to non-public data. SSRF to cloud metadata or localhost services. Unbounded SQL/NoSQL exfil. Arbitrary file read. Password, API key, session token, or other users' PII in response bodies or logs.
mediumSink reachable but requires a plausible precondition (auth, known ID). Data leak of non-secret but unintended internal fields (feature flags, internal IDs, roles). Path traversal bounded by a directory without full normalization.
lowDefense-in-depth gap. Verbose error page in a non-user-facing service. Log fields that include internal identifiers not considered sensitive. Report only when the thread is clear.

Pick the lower level when in doubt and explain why.

What to Report

SSRF

  • requests.get(user_url), urllib.urlopen(user_url), axios.get(user_url), fetch(user_url), got(user_url), http.client.HTTPConnection(user_host) without an allowlist and IP-literal / DNS-rebinding defenses.
  • Webhook or integration subscriber that POSTs to a callback_url field from the payload without re-validating.
  • Image / preview proxy with user-controlled URL and no allowlist.
  • Next.js /_next/image with remotePatterns: '**' (GHSA-rvpw-p7vw-wj3m), Next.js Server Actions SSRF (CVE-2024-34351).
  • String-based URL blocklists (defeated by DNS rebinding, IP-literal encoding).
  • Substring-prefix hostname allowlists (defeated by api.partner.com.attacker.com).
  • Redirect-following fetches without re-validation each hop (axios CVE-2020-28168).
  • IMDS bypass via header injection (CVE-2026-40175 axios header-pollution gadget).

High-value internal targets: 169.254.169.254 (AWS/Azure metadata), metadata.google.internal (GCP), 127.0.0.0/8, 10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16, 169.254.0.0/16. Real: Capital One 2019, Exchange CVE-2021-26855, Ivanti CVE-2024-21893, Shopify H1 #341876.

Path traversal / zip-slip

  • send_file(os.path.join(base, user_name)) without realpath containment.
  • res.sendFile(path.join(root, req.query.x)) without startsWith(root) check.
  • open(user_path), fs.readFile(user_path), fopen($user_path) without validation.
  • Archive extraction: tarfile.extractall without filter="data" (CVE-2007-4559 and offspring), zipfile.extractall on untrusted archives without per-entry validation, jszip / adm-zip / node-tar without containment check.
  • Static serving with a non-literal root (express.static(variable)).
  • Uploads where filename is used in the storage path without basename / UUID replacement.

Real: tarfile CVE-2007-4559, jszip CVE-2022-48285, node-static CVE-2023-26111.

SQL / NoSQL injection (bulk exfil angle)

  • Django .raw(f"..."), .extra(where=[f"..."]), RawSQL(f"..."), cursor.execute(f"...") with user interpolation.
  • SQLAlchemy text(f"..."), session.execute(f"...").
  • Sequelize literal() with user data (CVE-2023-25813), sequelize.query(...${user}...).
  • Prisma $queryRawUnsafe / $executeRawUnsafe with user data.
  • Mongoose populate({match: userObj}) (CVE-2025-23061), unsanitized request-body objects as Mongo filters, $where with user string.
  • Postgres JSON-operator splicing: SELECT data -> '${userKey}' FROM ....

Authorization bypass via SQL, such as returning another user's row, is out of scope unless injection itself is the enabler.

XXE (file-read and SSRF angle)

  • Python stdlib xml.etree, xml.sax, xml.dom.minidom on untrusted XML (defaults resolve entities).
  • lxml.etree.fromstring(user_xml) without resolve_entities=False / no_network=True (CVE-2024-6508 family).
  • Java DocumentBuilder without disallow-doctype-decl and the four companion feature flags.
  • Node libxmljs with { noent: true } on user XML.
  • .NET XmlDocument without XmlResolver = null, XmlReader without DtdProcessing = Prohibit.
  • SAML / OOXML / SOAP parsers that wrap an unsafe XML stage.

Response, log, and export leakage

  • Pydantic response model with Config.extra = "allow" (Sentry 0c0aae90ac1 was this).
  • DRF ModelSerializer with fields = '__all__' exposing password hashes, API tokens, internal flags via future migrations.
  • FastAPI endpoint missing response_model — raw ORM row shipped to client.
  • Prisma / Sequelize / Mongoose queries returning full rows without select or DTO filtering.
  • GraphQL introspection enabled in production. Apollo debug: true in production.
  • Flask DEBUG = True / Django DEBUG = True / Werkzeug debugger in production.
  • Error handlers returning err.stack, err.message with SQL fragments or internal paths, traceback.format_exc() in response bodies.
  • Loggers capturing request.body, request.headers, payload objects wholesale.
  • CSV exports without formula-prefix escape (=, +, -, @, tab, CR). Real: Tendenci #919.
  • Tarball / JSON exports bundling cross-resource data without per-resource authorization re-verification.
  • Download links with long expiry and no single-use enforcement.

What NOT to Report

  • Code execution (command injection, deserialization RCE, SSTI, eval, prototype-pollution reaching code sink).
  • Authorization (IDOR via straightforward missing scoping, tenant or role boundary failures). Flag here only when a bulk-exfil primitive, such as a mass query or enumeration via IDOR, is enabled.
  • XSS, CSRF, crypto primitive misuse, secrets hard-coded in source, transport security.
  • DoS / ReDoS unless it produces a data-exfil primitive (timing-based NoSQL $where oracle).
  • Dependency CVEs as a class.

False-Positive Traps

  1. Fetches to a hardcoded URL constant are not SSRF.
  2. Fetches to a literal allowlist (if host in ALLOWED_HOSTS:) with exact-hostname match are not SSRF. Substring match is.
  3. yaml.safe_load, JSON.parse, json.loads are not deserialization RCE — and not exfil either unless downstream.
  4. send_from_directory (Flask) applies safe_join. Generally safe.
  5. express.static('public') with a literal root is safe.
  6. res.sendFile(name, { root }) with a literal root and user name benefits from Express-level .. rejection; still audit.
  7. response_model=SomeModel (FastAPI) is a defense.
  8. DRF ModelSerializer fields = '__all__' on a ReadOnlyModelViewSet is over-exposure but not mass-assignment.
  9. Error handler returning {"error": "internal"} with separate server-side log is the safe shape.
  10. Sentry callers that go through safe_urlopen / safe_urlread already pass through the IP check chain.
  11. Prisma tagged-template $queryRaw parameterizes automatically.
  12. Principal-derived IDs (WHERE user_id = request.user.id) are not IDOR; they are an authorization concern, not exfil.

Canonical Patterns

Pattern: SSRF to cloud metadata

Real: Capital One 2019, Next.js CVE-2024-34351.

Python - bad:

python
resp = requests.get(user_url, allow_redirects=True)

Python - safe (Sentry-style):

python
from sentry.http import safe_urlopen
resp = safe_urlopen(user_url, allow_redirects=False)

TypeScript - bad:

ts
const resp = await fetch(userUrl);
const resp = await axios.get(userUrl);   // Historical redirect-bypass CVE-2020-28168.

TypeScript - safe:

ts
const parsed = new URL(userUrl);
if (!ALLOWED_HOSTS.has(parsed.hostname)) throw new Error('disallowed');
const { address } = await lookup(parsed.hostname);
const ip = ipaddr.parse(address);
if (['private','linkLocal','loopback','uniqueLocal','unspecified'].includes(ip.range())) {
  throw new Error('disallowed ip');
}
const resp = await fetch(parsed, { redirect: 'manual' });

Pattern: Path traversal in file download

Python - bad:

python
return send_file(os.path.join("/var/app/exports", name))

Python - safe:

python
base = Path("/var/app/exports").resolve()
target = (base / name).resolve()
if not target.is_relative_to(base):
    abort(403)
return send_file(target)

TypeScript - bad:

ts
res.sendFile(path.join('/var/app/exports', String(req.query.name)));

TypeScript - safe:

ts
const base = path.resolve('/var/app/exports');
const target = path.resolve(base, String(req.query.name));
if (!target.startsWith(base + path.sep)) return res.sendStatus(403);
res.sendFile(target);

Pattern: Raw SQL with user interpolation

Python - bad:

python
Invoice.objects.extra(where=[f"customer_id = {request.GET['cid']}"])

Python - safe:

python
Invoice.objects.filter(customer_id=request.GET["cid"])
# or, when the ORM can't express it:
Invoice.objects.extra(where=["customer_id = %s"], params=[request.GET["cid"]])

TypeScript - bad:

ts
await prisma.$queryRawUnsafe(`SELECT * FROM users WHERE name = '${name}'`);

TypeScript - safe:

ts
await prisma.$queryRaw`SELECT * FROM users WHERE name = ${name}`;

Pattern: Response leaks internal fields

Real: Sentry commit 0c0aae90ac1 (Pydantic extra = "allow" leaked SeerRunState fields).

Python - bad:

python
class SeerState(BaseModel):
    class Config:
        extra = "allow"
    user_query: str

@app.post("/chat")
def chat(state: SeerState):
    return state.dict()

Python - safe:

python
class SeerState(BaseModel):
    class Config:
        extra = "ignore"
    user_query: str
    internal_trace: str = Field(exclude=True)

TypeScript (Prisma) - bad:

ts
const user = await prisma.user.findUnique({ where: { id } });
return res.json(user);   // password_hash, 2fa_secret, internal flags.

TypeScript - safe:

ts
const user = await prisma.user.findUnique({
  where: { id },
  select: { id: true, displayName: true, avatarUrl: true },
});
return res.json(user);

Pattern: XXE file read

Python - bad:

python
import xml.etree.ElementTree as ET
tree = ET.fromstring(request.data)

Python - safe:

python
from defusedxml import ElementTree as ET
tree = ET.fromstring(request.data)

Investigation Playbook

  1. Classify the sink. Load the matching reference.
  2. Trace the source. Is the value at the sink user-controlled?
  3. Check the allowlist / validator / parameterization between source and sink.
  4. Check the library version. axios / lxml / Sequelize / Prisma / Flask versions change the analysis.
  5. Check siblings and history. rg -n '<sink>' for every call site. git log -p <file> for recent guard removals.

If the thread cannot be resolved, drop the finding or report with lower confidence.

Output

For each finding:

  • File and line of the unsafe code.
  • Severity from the table above.
  • Sink class (SSRF, path traversal, SQL injection, XXE, response leak).
  • What is wrong, in one sentence.
  • Source: where the attacker-controlled value originates.
  • Sink: which API produces the disclosure.
  • Trace: the specific path from source to sink.
  • Impact: what data can be read, by whom.
  • Fix: the concrete change. Name the allowlist, the required parameter, the missing containment check, the DTO/select.

Group findings by severity. Lead with high.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Wrdn Data Exfil AI skill do?

Detects bugs where untrusted input reaches a sink that leaks data beyond its intended scope. Covers SSRF (including cloud metadata, internal services, image proxies), path traversal and archive zip-slip, SQL/NoSQL injection enabling bulk reads, XXE file read, response serializers over-exposing internal fields, verbose error pages, logs capturing secrets, and CSV/formula injection in exports. Run on any diff touching HTTP clients with user URLs, file I/O with user paths, raw queries, XML parsing, response serializers, error handlers, or export pipelines.

Why use Wrdn Data Exfil on TypingMind?

Because you install it once and use it with any model. Wrdn Data Exfil 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 Wrdn Data Exfil in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/getsentry/warden-skills/tree/main/skills/wrdn-data-exfil. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Wrdn Data Exfil?

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 Wrdn Data Exfil?

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

Is the Wrdn Data Exfil AI skill free?

Yes. It is published on GitHub by getsentry under the MIT license. 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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