Benchmark
Detection you can check, not take on faith.
Every buyer asks the same two questions first: how much does it find, and how often is it wrong? We answer both as measured numbers — and show how the platform compares with the SAST, DAST, SCA and IAST tools you already know.
83–89%
of planted flaws found by an active dynamic scan (quick and full depth) on our corpus
100%
of a genuine cross-account data-exposure case found by the access-control check, at every depth
0%
of the clean look-alike records wrongly flagged by that same check
Platform coverage
One platform, five disciplines
Most tools do one job. CodesGuard runs static analysis, dependency scanning, dynamic testing, runtime instrumentation and AI-driven penetration testing against the same project, in one place.
How much more we find
Measured against the tools you already know
In our own testing, CodesGuard found up to 95% more real vulnerabilities than mid-range SAST and DAST scanners — including on custom frameworks, where pattern-based tools lose the thread.
SAST
Injection, auth, crypto, access control
SCA
Reachable, exploitable advisories
DAST
Active, authenticated attack coverage
IAST
Runtime-confirmed at request time
Custom frameworks
Bespoke / in-house framework code
Share of known flaws found on our own test applications, CodesGuard versus a typical mid-range scanner. The gap is widest on custom and in-house frameworks, where pattern- and rule-based tools have no signatures to match.
Built for custom frameworks
CodesGuard follows data through your code, so it understands bespoke and in-house frameworks that rule- and pattern-based scanners have no signatures for.
Proof, not just a rule hit
Where other scanners flag a pattern, every finding here carries the dataflow trace, the request and response, or the exploit that proves it is real.
Noise you can rank
An AI false-positive score orders every finding, so your team fixes the real criticals first instead of triaging a wall of undifferentiated alerts.
Where CodesGuard sits
Breadth of coverage, depth of proof
The two axes that decide a security platform: how much of your risk a single tool can see, and how far it traces and how hard it proves each finding.
Platform leaders — Broad coverage and deep, proven findings. Where a platform belongs.
Deep specialists — Deep analysis, but one discipline only — strong SAST, nothing else.
Broad suites — Several modules bolted together, shallower proof on each.
Point tools — A single-purpose tool for one slice of the problem.
Our own assessment of where the tools we tested sit — not an independent analyst rating. Breadth is how many disciplines a single tool covers; depth is how far it traces and how hard it proves each finding.
Manual, DAST, or AI pentest?
The reach of a human tester, at the speed of a scanner
A manual pentest is deep but slow and costly. A traditional DAST scan is fast but shallow on business logic and light on proof. CodesGuard's AI pentest is built to take the best of both.
Manual pentest
Deep and creative, with real exploit proof — but slow, costly, and only as consistent as the tester and the calendar allow.
Traditional DAST
Fast, cheap and repeatable — but pattern-driven, weak on business logic, and often stops at “potential” without proof.
CodesGuard AI Pentest
Reasoning agents drive real tools through a full kill chain: the speed and consistency of automation with the exploit proof and business-logic depth of a human tester.
How we measure
Measured, not asserted
The method matters more than any single percentage — because your engineers will ask how every number was produced.
Planted flaws and a clean twin
One application has flaws deliberately planted in it, with an answer key naming every one. A second is a route-for-route copy with nothing planted. We measure how much is found on the first and how often we are wrong on the second.
Scored per class, at every depth
Each vulnerability class is scored independently at each scan depth, so coverage is a set of measured numbers — not a single headline figure that hides the detail.
Noise is measured, not ignored
False positives are counted on the clean control. A tool that finds everything by flagging everything scores badly here, which is the point.
Re-scored on every change
The whole corpus runs in CI. A regression in any class fails the build before it can reach you, so the numbers stay honest over time rather than being a one-off slide.
These figures come from our own continuously-scored dynamic-testing corpus. The complete per-class report — and the methodology behind static analysis, dependency and AI-pentest results — is shared during a technical evaluation, where your engineers can see exactly how each number was produced.
See the full measured report
Ask for the per-class detection report, the competitor comparison and a walkthrough of how every number is produced during a technical evaluation.