Walid Boulanouar recently shared something that caught my attention: "claude code for hacking is here 🤯". In the same breath, he introduced "shannon", which he described as "a fully autonomous ai hacker that scans your web app and finds real security holes before attackers do".

That framing matters. Walid is not talking about another chatbot that suggests secure coding tips after the fact. He is talking about an agent that actively probes your application, maps attack surfaces, follows vulnerable code paths, and returns actionable findings back to your coding environment so you can fix issues fast.

In this post, I want to expand on what Walid pointed out and explore why autonomous security agents like Shannon are a big deal for modern teams, especially for fast-moving builders.

The shift: from AI that writes code to AI that attacks it

Walid summed up the bigger trend with a simple line: "ai is not just writing code now - it is reviewing and stress testing it too".

That is the key. Over the last two years, the dominant story has been AI-assisted development: generate boilerplate, refactor functions, scaffold endpoints, ship quickly. But shipping quickly changes the risk profile:

More code gets produced by fewer people
Less time is allocated to security reviews
Framework defaults and copy-pasted snippets spread insecure patterns
Teams rely on late-stage testing to catch issues

Autonomous offensive testing flips the script. Instead of asking, "Did we remember to test security?", you build a workflow where security testing happens continuously, using an attacker mindset, without waiting for a specialist to be available.

"you can ship fast then let it check what you broke" - Walid Boulanouar

That line resonates because it matches reality. Many teams cannot afford a full manual review for every iteration, but they also cannot afford to ignore security.

What Shannon is (and what "autonomous" should mean)

Walid described Shannon as "fully autonomous" and highlighted that it "runs deep automated recon and testing".

In practical terms, autonomy in a web app security agent usually implies:

Reconnaissance: Discovering endpoints, parameters, auth flows, and tech stack signals.
Threat modeling on the fly: Inferring likely weakness classes based on observed behavior (for example, where injection, access control, or file handling might exist).
Exploitation attempts: Trying safe but meaningful payloads to validate whether an issue is real.
Evidence gathering: Capturing the requests, responses, and steps needed to reproduce.
Reporting: Producing findings that developers can act on without translating security jargon.

The difference between "scanning" and "finding real security holes" is validation. Many classic scanners generate long lists of potential issues. Walid is highlighting something more useful: uncovering vulnerable code paths and returning them in a way that leads directly to a patch.

Why this is especially useful for vibe coders and new developers

Walid called out a specific audience: "it is especially great for vibe coders and new people getting into coding".

I read that as: people who can build fast using AI and modern frameworks, but who may not have deep security intuition yet.

New developers typically struggle with:

Authentication and session handling edge cases
Authorization (who can do what, under which conditions)
Input validation and output encoding
Dangerous defaults in dependencies
Misconfigured cloud storage, webhooks, or admin panels

A good autonomous tester can become a feedback loop. You build a feature, run the agent, and it tells you what a real attacker would try next.

That feedback loop is not just about catching bugs. It trains developers to think in adversarial terms. Over time, the team internalizes patterns like "any endpoint that returns data should be tested for IDOR" or "any file upload flow needs strict content validation".

The Claude Code loop: findings that flow back into fixes

Walid mentioned something important about workflow integration: Shannon "gives them back to claude code so you can patch and improve fast".

This is where agentic security becomes more than a scanning tool. The ideal loop looks like this:

Shannon identifies an exploitable issue and provides reproduction steps.
Those steps are handed to your coding assistant (in Walid's example, Claude Code).
The assistant helps you locate the relevant code path, implement a fix, and write tests.
Shannon reruns the check to verify the patch.

If done well, you end up with a tight, developer-native remediation cycle: discover, fix, verify, regress.

A concrete example of the loop (conceptually)

Imagine Shannon finds that changing a numeric ID in an API request returns another user's data. Instead of producing a vague warning, it should:

Show the exact request that succeeded
Explain why the authorization check is missing or insufficient
Point to the endpoint and suspected code path
Suggest what a robust authorization check should validate

Then Claude Code can help you implement policy checks, add authorization middleware, and add a regression test that fails if the same bypass ever returns.

Benchmarks and speed: what the numbers actually imply

Walid cited strong performance signals:

"scored 96.15 percent on xbow higher than typical human results around 85 percent"
"around 10x faster than classic manual security reviews"

Benchmarks are never the whole story, but they do suggest two meaningful things.

First, capability is moving fast. If an autonomous agent is outperforming typical human benchmark results in a defined evaluation, it indicates that the agent can systematically cover common vulnerability classes and avoid missing obvious paths.

Second, speed changes behavior. Manual reviews are expensive and therefore rare. A tool that is 10x faster can be run more often:

On every pull request for critical services
Nightly against staging
Before major releases
After dependency updates

The win is not only that the agent is fast. The win is that frequent testing reduces the window of exposure between a vulnerability being introduced and being found.

Open source matters for security tools

Walid emphasized: "fully open source - anyone can inspect test and improve it".

For security tooling, open source can be a feature, not a risk, when managed responsibly:

Transparency: Teams can validate what the agent is doing and how it stores data.
Extensibility: You can add custom checks for your stack and threat model.
Reproducibility: You can run it in your environment without sending sensitive traffic to third parties.

There are tradeoffs. Attackers can also inspect the code. But that is true for many widely used security tools and libraries. The key question is whether your defense benefits more from visibility, rapid iteration, and community review than it loses from code exposure.

A practical way to use an autonomous hacker responsibly

If you want to apply the idea Walid shared, here is a simple, pragmatic approach that fits most teams.

1) Start in staging, not production

Run Shannon against a staging environment with realistic data and feature flags. Make sure rate limiting and logging are in place so testing does not look like an actual incident.

2) Focus on high-impact areas first

Prioritize:

Auth flows (login, password reset, OAuth)
User data endpoints
Admin functionality
File uploads and document processing
Webhooks and integrations

3) Treat findings like engineering work, not alerts

For each validated issue:

Create a ticket with reproduction steps and evidence
Patch with the smallest correct fix
Add a regression test
Rerun the agent to confirm closure

4) Use it as a teaching tool

When the agent finds something, review it as a team. Ask:

What assumption did we make that was wrong?
What guardrail could prevent this class of bug?
Should we add a lint rule, middleware, or secure default?

Limits and cautions (so you do not overtrust it)

Autonomous testing is powerful, but it is not magic.

False positives and false negatives still exist. You need human judgment for triage.
Some bugs require business context. Logic flaws, payment abuse, and subtle authorization rules often need domain knowledge.
Agents can be noisy if environments are unstable. Good logging and reproducible environments matter.
Security is not only testing. You still need secure design, least privilege, and monitoring.

The healthiest mindset is: use an autonomous agent as a force multiplier, not as permission to skip security thinking.

Closing thoughts

Walid Boulanouar's post captured a moment that is easy to miss: the same AI wave that helped us ship faster is now being aimed at breaking what we ship, on purpose, so we can harden it.

If tools like Shannon deliver on the promise Walid described, they will become a standard part of modern development: always-on, adversarial, and tightly integrated into the build-fix-verify loop.

This blog post expands on a viral LinkedIn post by Walid Boulanouar, building more agents than you can count | aiCTO ay automate & humanoidz | building with n8n, a2a, cursor & ☕ | advisor | first ai agents talent recruiter. View the original LinkedIn post →