For Cybersecurity Professionals
You audit systems for clients. Who audits your code?
We scan, then we forget. You keep what you want.
You find the SQL injection, the broken auth, the exposed secret — that is the job. But the work product you hand back is also code: the proof-of-concept exploit, the automation script, the parsing tool, the remediation snippet you wrote so the client could fix the finding. That code runs in client environments, often with elevated access, and it is rarely held to the standard you just applied to theirs. A remediation script that itself concatenates input into a query, a quick Python helper that shells out with os.system(), a PoC with a hardcoded API token left in — a security professional shipping a vulnerable artifact is the sharpest irony in the industry, and the one that ends engagements. AllScanTool is the fast second set of eyes on your own output: paste the script before it leaves your machine and the Delivery-Layer Engine flags injection, XSS, hardcoded credentials, and insecure functions — each with the line, a severity, and a fix. It runs in your browser and forgets the code the moment you leave. No logs. No storage. No retention. Your code stays private.
You hold client code to a standard. Your own deliverables deserve the same pass.
These are the places where the code a security professional writes can carry the very flaws they were hired to find.
Remediation code that introduces its own flaw
You write the fix snippet so the client can close the finding. If that snippet builds a query by concatenation or echoes input unescaped, you have shipped a new vulnerability inside the patch.
Automation and tooling written under time pressure
Scrapers, parsers, and engagement helpers get written fast to hit a deadline. eval(), unsanitised subprocess calls, and unsafe deserialization slip into the very tools you run inside client networks.
Hardcoded tokens and creds in PoCs and scripts
An API key, a test credential, a callback host wired straight into a proof-of-concept. Handed to the client or pushed to a repo, that artifact becomes the exposure you would flag in anyone else’s code.
Command and injection sinks in your helpers
A quick script that calls os.system() or shell_exec() on a value you built from input is the exact command-injection pattern you write up in findings — now running with your privileges.
AI-generated exploit and tooling code, unverified
LLMs speed up writing parsers, payloads, and glue code. The output looks authoritative, but it carries the same hidden injection and insecure-function risks — in code you will run against a live target.
Your name and your credibility are on it
You are the security expert in the room. A vulnerable deliverable does not just cause an incident — it undermines the entire engagement and the trust your reputation is built on.
Ask AST
The questions penetration testers, security consultants, and freelance auditors run into when their own deliverable is the code — answered with the specific line, the severity, and the fix.
QI wrote a remediation snippet for a client to fix a SQL injection finding — can you confirm my fix does not introduce one of its own?
Paste it and AST checks that the query is properly parameterized, flags any remaining concatenation with the line and severity, and shows the safe form — so the patch you hand back closes the finding instead of relocating it.
QI built a Python automation helper for an engagement that shells out to the OS — does it have a command-injection problem?
Paste the script. AST flags os.system() and subprocess calls built from unsanitised input, marks the severity, and shows how to pass arguments safely — before you run that tool inside a client network.
QI want to make sure there are no hardcoded tokens or test creds left in this PoC before I hand it to the client.
Paste the code and AST surfaces hardcoded API keys, credentials, and callback hosts with the exact line — so your proof-of-concept does not become the next exposed-secret finding in someone else’s report.
QI had an LLM generate a parser for engagement data that uses eval() — what are the risks before I run it against live output?
AST flags the eval() and any unsafe deserialization, explains the injection path, marks the severity, and shows the safe parsing approach — so AI-assisted tooling is verified before it touches a real target.