Use case: legacy code
The code nobody remembers writing is now the easiest code to attack
Finding a vulnerability in an old codebase used to take an attacker weeks of manual reading. With AI, it takes minutes. Find them first.
Finding a vulnerability in an old codebase used to take an attacker weeks of manual reading. With AI, it takes minutes. Every legacy application that was "too big to review" is now exposed to review by the wrong people first.
- Review at the scale of the whole codebaseThe sharded scan engine splits large repositories across parallel workers, so a million-line application gets the same line-by-line attention as a small one.
- Any language, any ageAnalysis is driven by understanding code, not by rule packs per language. PHP, .NET and VB, Java, JavaScript, Python and mixed stacks are all in scope.
- Start with an inventory, not a guessKnow the shape of the system first: file counts, languages, largest and most recently changed files. Then define focus areas for the payment, authentication and data-handling modules.
- Resolve the "is this even used?" questionDead code is the biggest source of false alarms in legacy reviews. Analysts post a validation question and the developer who knows answers it inside the finding.
- A remediation plan the business can followFindings ranked by risk score, grouped by file, assigned to owners, with progress tracked over months, not just a one-time report.
What a legacy review looks like
Every file
inventoried before analysis begins
Focus areas
deep-dive the modules that carry the risk
Any stack
no language rule packs required
Typical findings in older applications
- CriticalSQL built by string concatenation in a reporting module, reachable from a forgotten admin page
- CriticalHard-coded credentials and API keys committed years ago and still valid
- HighSession and authentication logic that predates modern hashing,
md5or plain comparisons - HighFile upload handlers that trust the client-supplied name and type
- MediumVerbose error output and debug endpoints left enabled in production paths
Why analyze the source
More accurate than external testing. Less noise than traditional static analysis.
Analyzing source code directly reveals vulnerabilities that outside-in scanning cannot see. AI-driven analysis understands code context, so your team spends time fixing real issues instead of chasing false positives.
| External pen test | Traditional SAST | SourceScan.ai | |
|---|---|---|---|
| What it sees | The running application, from the outside | Source code, matched against fixed rule patterns | Source code, read line by line with full context of how it is called |
| Unreachable or hidden paths | Missed if not exposed during the test | Flagged, but without knowing if they matter | Found and explained, including dead-code checks with your developers |
| False positives | Low, but coverage is narrow | High. Rules cannot tell safe from unsafe usage | Low. Every finding is analyzed in context and confirmed by an expert verdict |
| Cadence | Once or twice a year, as a project | Per build, results usually ignored | Continuous. Re-check when code changes, with a workflow that closes findings |
| Output | A PDF and a debrief | A long list of alerts | Prioritized findings with owners, status, verification and an audit trail |
| Who it serves | Security team | Developers who set it up | Decision makers, programmers and security experts together |
Common questions
More answers on the FAQ page.
Who does the expert analysis?
Your own security team can use the platform directly. If you do not have one, our partner cybersecurity analysts perform the deep analysis and verdicts, and your developers and decision makers work with the results in the same dashboard.
What languages and frameworks are supported?
Analysis is driven by understanding code rather than by per-language rule packs, so mixed and unusual stacks are fine. Current engagements include PHP, .NET and VB, Java, JavaScript and TypeScript, and Python codebases, including very old ones.
How large a codebase can it handle?
Large ones. The advanced scan shards the repository across parallel workers and deduplicates findings across shards and across scans. You always see the estimated scope and cost before starting, and you can begin with focus areas on the highest-risk modules.
Deeper than a pen test
Ready to find out what is really in that old codebase?
Bring one legacy repository. We will inventory it, scan the highest-risk modules and show you the remediation workflow on your own code.
Or call 1-877-342-8592