CNRT CodeLens AI source code vulnerability scanner · Quantum Ventura, makers of CyberNeuro-RT (CNRT) · CyberNeuro-RT real-time AI network defense · AI-driven network intrusion detection · real-time cyber threat detection Quantum Ventura
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AI vulnerability scanning

CNRT CodeLens

Most vulnerability scanners hand back a list too long to work through and too noisy to trust. CodeLens is an AI vulnerability scanner built around the two things that make a finding useful: checking its own work, and stopping short of changing your code without being asked.

VerifiedA second model checks the first
ResumableA crash costs one stage, not the scan
Opt-inIt never edits without being asked
CNRT CodeLens · auth/session.c
READY
auth/ session.c token.cnet/ parse.c frame.c
auth/session.c
208 size_t need = hdr->len + sizeof(tag_t);
209 if (need > cap) {
210 cap = need;
211 }
212 
213 memcpy(buf, hdr->body, hdr->len);
214 buf[hdr->len] = 0;
215 
216 return finalize(buf, need);
217}
Reachable

Unchecked bounds on copy path

Length comes from the caller and is used to write before it is compared against the destination size.

213line Highseverity 2call sites
Nine stages, resumable
1Index repositoryqueued
2Resolve dependenciesqueued
3Build call graphqueued
4Multi-lens detectionqueued
5Cluster candidatesqueued
6Verify findingsqueued
7Rank by reachqueued
8Draft remediationqueued
9Export reportqueued
auth/session.c:213

Proposed change, for your review

212
213 - memcpy(buf, hdr->body, hdr->len);
213 + if (hdr->len >= cap) return E_RANGE;
214 + memcpy(buf, hdr->body, hdr->len);
ApplySkip Export as MarkdownExport as PDF
The problem

Vulnerability scanners return lists too long to work through and too noisy to trust, so the vulnerabilities that matter get lost.

Our answer

CodeLens ranks vulnerabilities by whether an attacker can reach them, has a second model check each one, and leaves the fix to you.

Verification

A second model checks what the first one found

Detection and verification are separate stages by design. The first pass reads the repository through several different lenses; a later stage takes each candidate finding and asks a different model whether it actually holds. What reaches you has survived being argued with, which is what makes a short list worth reading.

CNRT CodeLens · Verification
ReviewFindingsHistory
STAGE 6
auth/session.c:213

Unchecked bounds on copy path

FIRST PASS CANDIDATE

Length reaches a write before it is compared against the destination size.

SECOND MODEL CONFIRMED

Reachable from two call sites that accept caller-controlled input. Holds.

Rejected at this stage
util/log.c:33 Format string is compile-time constant DROPPED
net/frame.c:88 Guarded by a check the first pass missed DROPPED
Recovery

A crash costs you one stage, not the whole scan

A full pass over a large repository is long-running work, and long-running work fails. CodeLens checkpoints after each stage, so an interrupted scan picks up where it stopped rather than starting from nothing. On a large codebase that is the difference between a re-run and a lost afternoon.

CNRT CodeLens · Run 1184
ReviewRunHistory
RESUMED
✓1Index repository00:41
✓2Resolve dependencies01:12
✓3Build call graph02:38
✓4Multi-lens detection08:56
✓5Cluster candidates01:04
6Verify findingsRESUMED HERE
7Rank by reachqueued
8Draft remediationqueued
9Export reportqueued
Control

It does not touch your code unless you ask

Detection runs first and stops. Remediation is a separate, deliberate step you choose to take, per finding. Plenty of tools will open pull requests against your repository on their own; this one reports, and waits. Findings export as Markdown or PDF so they can go into a review or a report as they are.

CNRT CodeLens · Remediation
ReviewFixesHistory
NOT APPLIED
auth/session.c:213

Proposed change, for your review

211 }
212
213 - memcpy(buf, hdr->body, hdr->len);
213 + if (hdr->len >= cap) return E_RANGE;
214 + memcpy(buf, hdr->body, hdr->len);
215 buf[hdr->len] = 0;
ApplySkip Export as MarkdownExport as PDF
CNRT CodeLens

Technical specifications.

Built at Quantum Ventura and sold on its own. It reads source code rather than network traffic, so it stands as its own product, and it reports into the CyberNeuro-RT dashboard for teams already running it.

PositionStandalone product, integrated with the CNRT dashboard
VerificationSeparate model pass over candidate findings
RecoveryCheckpoint and resume between stages
OutputMarkdown and PDF
InterfaceMultilingual, English and Japanese in place today

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