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Wrdn Code Execution

Organization
getsentry
wrdn-code-execution

Detects bugs where untrusted input reaches a sink that produces code or command execution on the server. Covers command/shell injection, unsafe deserialization, server-side template injection, eval/Function/vm reached by user data, XXE-to-RCE gadgets, and prototype pollution that lands on a code-executing sink. Run on any diff touching subprocess/exec calls, template rendering, deserialization of bytes, XML parsing, or deep-merge of user-controlled objects.

Overview

Publishergetsentry
Repositorywarden-skills
Skill namewrdn-code-execution
Stars
58
Forks
3
Bundled files
11
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.

  • 11 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 Code Execution 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-code-execution
  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-code-execution .claude/skills/wrdn-code-execution
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wrdn Code Execution 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 Code Execution 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 Code Execution 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 untrusted input reaches a sink that executes code on the server. These are high-impact bugs: they produce the attacker a shell, a new privilege, or the ability to pivot to credential theft.

The abstract shape is constant across languages:

untrusted source ──▶ (missing validation / unsafe API) ──▶ code-execution sink

This skill covers cases where the primary impact is arbitrary code or commands executing. Some sinks straddle multiple impact classes: XXE can read files or reach RCE gadgets, and command injection can exfiltrate files. Report here only when the code-execution 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 in the form used? subprocess.run(["ls", user_arg]) is safe. subprocess.run(f"ls {user_arg}", shell=True) is not.
  • Identify the source. Values from request.body, request.query, request.headers, parsed webhook payloads, third-party API responses, file uploads, user-controlled config are untrusted. Hardcoded constants and server-side-derived values are not.
  • Trace the path. Read the function, the caller, and whatever validation sits between. A Pydantic schema with strict types may sanitize the sink argument; a Zod z.string() with no regex may not.
  • Check the library version. yaml.load without SafeLoader on PyYAML < 5.1 is unsafe by default. jsonwebtoken.verify before 9.0 allows algorithm confusion. vm2 in any version is abandoned and RCE-prone.
  • Use the shell. git log -p <file> shows whether a validation step was recently removed. rg -n '<sink>' enumerates siblings so you can compare the analysis.
  • Detect the framework. Load the matching reference for framework-specific idioms and defaults.

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

References

Load on demand. Most diffs do not require opening any reference.

By sink class

WhenRead
Shell / subprocess / child_process.exec / Runtime.exec${CLAUDE_SKILL_ROOT}/references/command-injection.md
pickle, yaml.load, node-serialize, Java native, BinaryFormatter, unserialize${CLAUDE_SKILL_ROOT}/references/deserialization.md
render_template_string, Template(user_source), Handlebars, Pug, Freemarker${CLAUDE_SKILL_ROOT}/references/ssti.md
eval, exec, Function, vm.runInNewContext, compile, dynamic import${CLAUDE_SKILL_ROOT}/references/eval.md
Object.assign / lodash.merge / defaultsDeep / $.extend + RCE sink${CLAUDE_SKILL_ROOT}/references/prototype-pollution.md

By framework

WhenRead
Sentry core: integration webhooks, YAML loaders, runner eval/exec${CLAUDE_SKILL_ROOT}/references/sentry.md
Django views, templates, pickle session serializer${CLAUDE_SKILL_ROOT}/references/django.md
FastAPI: Jinja2 templates, BackgroundTasks with subprocess${CLAUDE_SKILL_ROOT}/references/fastapi.md
Flask: render_template_string, Werkzeug debugger, from_pyfile${CLAUDE_SKILL_ROOT}/references/flask.md
Express / Node: child_process, vm, template engines, dynamic require${CLAUDE_SKILL_ROOT}/references/express.md
Next.js: Server Actions with eval/Function, React2Shell (CVE-2025-55182)${CLAUDE_SKILL_ROOT}/references/nextjs.md

Severity

LevelCriteria
highUnauthenticated or low-privilege code execution. Unsafe deserialization of request bytes. SSTI with user-controlled template source. eval/Function/vm reached by request data. Shelled exec with user-interpolated command string.
mediumSink reachable but gated by authentication (still a finding; authenticated RCE is still RCE). Library in a version known to have mitigations but not the full fix. Prototype pollution with a plausible downstream sink not yet traced.
lowDefense-in-depth gap. Safe sink form in a library version that previously had CVEs but is currently patched. Report only when the thread is clear.

Pick the lower level when in doubt and explain why.

What to Report

Command / shell injection

  • os.system, os.popen, subprocess.run(..., shell=True), subprocess.Popen(shell=True), check_output(..., shell=True) with user-interpolated command.
  • child_process.exec, child_process.execSync, spawn(..., { shell: true }) with template-string command.
  • Windows: Node spawn/execFile targeting .bat/.cmd with user arguments on Node < 18.20 / 20.12 / 21.7 (CVE-2024-27980, BatBadBut).
  • Java Runtime.exec(String), ProcessBuilder("sh", "-c", userString).
  • Ruby system("... #{x}"), backticks with interpolation.
  • PHP system, shell_exec, exec, passthru, backticks with user data.
  • Argument injection without a shell: subprocess.run(["git", user_arg]) where user_arg can be --upload-pack=malicious — missing -- separator.

Real: CVE-2021-22205 (GitLab ExifTool), CVE-2024-27980 (Node BatBadBut).

Unsafe deserialization

  • Python pickle.loads, cloudpickle.loads, joblib.load, dill.loads, marshal.loads on bytes that trace to external input.
  • yaml.load without SafeLoader (PyYAML < 5.1 defaulted to unsafe). FullLoader before PyYAML 5.3.1 still allowed RCE gadgets (CVE-2020-1747).
  • Node node-serialize.unserialize (CVE-2017-5941, always unsafe).
  • Java ObjectInputStream.readObject on network input. Log4Shell (CVE-2021-44228). Spring4Shell (CVE-2022-22965).
  • .NET BinaryFormatter.Deserialize on untrusted bytes (deprecated in .NET 5+).
  • PHP unserialize on user data.
  • ML model files: torch.load, joblib.load, pickle.load on attacker-uploaded models (Picklescan CVE-2025-1716 pattern).

Server-side template injection

  • render_template_string(request.args[...]) (Flask SSTI canonical).
  • jinja2.Template(user_source).render(...).
  • Handlebars.compile(req.body.template) (CVE-2019-19919 prototype pollution → compile-time RCE).
  • pug.compile(user_source), ejs.render(user_template, ...).
  • Freemarker / Velocity with user-controlled template source; <#assign value="freemarker.template.utility.Execute"?new()> gadget.

Jinja2 SandboxedEnvironment has a history of bypasses (CVE-2019-10906, CVE-2016-10745). Treat sandbox as soft armor, not prevention.

eval / Function / vm reached by user input

  • Python eval, exec, compile with request data.
  • JS eval, new Function, new AsyncFunction, setTimeout(string_arg, ...), setInterval(string_arg, ...).
  • Node vm.runInThisContext (shares realm; RCE), vm.runInNewContext (pseudo-sandbox; escape-prone).
  • vm2 in any version — abandoned after CVE-2023-37903.
  • Ruby eval, instance_eval, class_eval with user data; send(user_method, ...).
  • PHP eval($user); assert($string) before PHP 7.2.
  • Spring SpEL parser.parseExpression(user_expr).getValue().
  • Nashorn/GraalJS/Rhino scriptEngine.eval(user_script) on a JVM.
  • Dynamic import(user_specifier), require(user_path), importlib.import_module(user_name).

Real: CVE-2025-55182 (Next.js React2Shell), every vm2 CVE, every Spring4Shell lineage bug.

Prototype pollution reaching a code-execution sink

  • lodash.merge / mergeWith / defaultsDeep / set / setWith with user data (CVE-2019-10744, CVE-2020-8203).
  • jQuery.extend(true, ...) with user data (CVE-2019-11358).
  • Hand-rolled recursive merge that does not filter __proto__ / prototype / constructor.
  • Downstream sink: Handlebars template compile reading polluted helper (CVE-2019-19919), Function constructor reading polluted property, auth check that lands on polluted flag. axios header injection → IMDS bypass (CVE-2026-40175).

XXE with RCE gadgets

XXE is usually a file-read or SSRF issue. Report the RCE branch here only when the stack exposes code-loading or gadget execution:

  • Java XXE → classloader gadgets or JNDI lookup paths. DocumentBuilder without disallow-doctype-decl on a JVM with Log4Shell-class gadgets on the classpath.
  • XSLT extensions that invoke system calls (xsl:invoke-java, legacy PHP XSL extensions).

Most XXE finds file exfiltration; flag the RCE branch when the stack is Java with deserialization gadgets present.

What NOT to Report

  • Data exfiltration (SSRF, path traversal, SQL/NoSQL injection enabling bulk reads, response field leakage).
  • Authorization (IDOR, missing ownership checks, role or tenant escalation, mass assignment enabling role elevation).
  • XSS, CSRF, crypto primitive misuse, secrets in source, transport security.
  • DoS / ReDoS unless it directly enables a code-execution sink.
  • Dependency CVEs as a class.

False-Positive Traps

  1. yaml.safe_load / yaml.load(..., Loader=SafeLoader) is safe. Only plain yaml.load on old PyYAML or explicit unsafe loaders are RCE.
  2. subprocess.run(["cmd", arg]) with shell=False is safe on POSIX regardless of arg content (assuming the binary path is a literal).
  3. execFile('bin', [userArg]) on Linux/macOS is safe. Windows .bat/.cmd targets implicitly shell on old Node (CVE-2024-27980).
  4. render_template("file.html", user=user) with a literal filename is safe. Only render_template_string(user_input) or Template(user_input) is SSTI.
  5. eval inside tests/, example notebooks, or an explicit sandboxed REPL is not production-reachable. Confirm the file role.
  6. ast.literal_eval is safe; parses literals without executing.
  7. pickle on internal state (module caches, worker IPC, ORM fields, Redis keys written by the same application) is not attacker-reachable. Sentry does this in arroyo, buffer/redis, gzippeddict. Confirm the source is internal before flagging.
  8. Template source from readFileSync('views/x.hbs') is safe. Only user-controlled template source is SSTI.
  9. Prisma.$queryRaw\...` (tagged template) is not an eval sink. Treat SQL injection as out of scope unless it reaches code execution.

Canonical Patterns

Pattern: Command injection via shelled exec

Real: CVE-2021-22205 (GitLab + ExifTool).

Python - bad:

python
subprocess.run(f"convert {user_filename} out.png", shell=True)
os.system(f"git clone {user_repo_url}")

Python - safe:

python
subprocess.run(["convert", user_filename, "out.png"], shell=False, check=True)
subprocess.run(["git", "clone", "--", user_repo_url], check=True)

TypeScript - bad:

ts
execSync(`git clone ${userRepoUrl}`);

TypeScript - safe:

ts
execFile('git', ['clone', '--', userRepoUrl]);

Pattern: Unsafe YAML load

Real: CVE-2020-1747 (PyYAML FullLoader).

Python - bad:

python
config = yaml.load(request.data)
config = yaml.load(request.data, Loader=yaml.Loader)

Python - safe:

python
config = yaml.safe_load(request.data)
config = yaml.load(request.data, Loader=yaml.SafeLoader)

Pattern: SSTI via user template source

Real: PortSwigger canonical, CVE-2019-10906 (Jinja2 sandbox escape).

Python (Flask) - bad:

python
@app.route("/preview")
def preview():
    return render_template_string(request.args["body"])

Python - safe:

python
return render_template("preview.html", body=request.args["body"])

TypeScript (Handlebars) - bad:

ts
const tmpl = Handlebars.compile(req.body.template);
res.send(tmpl({ user: req.user }));

TypeScript - safe:

ts
const tmpl = Handlebars.compile(readFileSync('preview.hbs', 'utf8'));
res.send(tmpl({ user: req.user }));

Pattern: eval / Function with user data

Real: CVE-2025-55182 (Next.js React2Shell), vm2 CVE-2023-29017/32314/37903.

Python - bad:

python
result = eval(request.args["expr"])

Python - safe:

python
import ast
result = ast.literal_eval(request.args["expr"])  # Literals only.

TypeScript - bad:

ts
const fn = new Function(req.body.code);
fn();

// Server Action:
'use server';
export async function run(userCode: string) {
  return new Function('return ' + userCode)();
}

TypeScript - safe:

ts
// Parse with a dedicated expression parser + allowlist of operations.
const result = safeEvaluator.evaluate(userExpr, { allow: ['+', '-', '*', '/'] });

Pattern: Pickle on untrusted input

Python - bad:

python
@app.post("/upload")
def upload():
    return pickle.loads(request.data)

Python - safe:

python
@app.post("/upload")
def upload():
    return json.loads(request.data)                 # JSON does not execute.

For ML model uploads: verify the source, enforce a file-format validator (no pickle), or use a restricted loader that rejects unknown opcodes.

Pattern: Prototype pollution → template RCE

Real: CVE-2019-19919 (Handlebars).

TypeScript - bad:

ts
// User supplies: {"__proto__": {"helperMissing": "function(){return process.mainModule.require('child_process').execSync('id');}"}}
const merged = _.merge({}, defaults, req.body);
const tmpl = Handlebars.compile('{{ nonexistent }}');
tmpl({});   // Reaches the polluted helperMissing; RCE at compile.

TypeScript - safe:

ts
// Validate shape before merge
import { z } from 'zod';
const Config = z.object({ theme: z.enum(['light', 'dark']).optional() });
const validated = Config.parse(req.body);
const merged = { ...defaults, ...validated };

Plus: upgrade Handlebars to 4.3.0+. Plus: upgrade lodash to 4.17.21+ (but don't rely on library fixes alone).

Investigation Playbook

  1. Classify the sink. Load the matching reference.
  2. Trace the source. Does the value at the sink come from external input?
  3. Check the library version. yaml.load / jsonwebtoken / vm2 / lodash versions change the analysis.
  4. Check for a validator. Pydantic, Zod, class-validator, schema validators can narrow the argument to a safe type.
  5. Check siblings and history. rg -n '<sink>' for every call site. git log -p <file> for recent validation 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 (command injection, deserialization, SSTI, eval, prototype pollution).
  • What is wrong, in one sentence.
  • Source: where the attacker-controlled value originates.
  • Sink: which API or operation produces code execution.
  • Trace: the specific path from source to sink.
  • Impact: what the attacker can execute (as whom, via what payload).
  • Fix: the concrete change. Name the safe API, the required validator, the missing parameter.

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 Code Execution AI skill do?

Detects bugs where untrusted input reaches a sink that produces code or command execution on the server. Covers command/shell injection, unsafe deserialization, server-side template injection, eval/Function/vm reached by user data, XXE-to-RCE gadgets, and prototype pollution that lands on a code-executing sink. Run on any diff touching subprocess/exec calls, template rendering, deserialization of bytes, XML parsing, or deep-merge of user-controlled objects.

Why use Wrdn Code Execution on TypingMind?

Because you install it once and use it with any model. Wrdn Code Execution 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 Code Execution in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/getsentry/warden-skills/tree/main/skills/wrdn-code-execution. 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 Code Execution?

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 Code Execution?

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

Is the Wrdn Code Execution 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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