Konvu vs Codex Security: Validation When Possible, or Exploit Proof by Default (2026)

Quick verdict: Run Codex Security to find vulnerabilities in your own code, and add Konvu when findings from Codex Security, your scanners and your bug bounty program outgrow your team. Codex Security validates findings when possible, while Konvu runs the exploit for every report against its own deployed copy of your application, then ships the fix and re-runs the exploit against it.

OpenAI's docs say Codex Security validates issues "in an isolated environment when possible" (Codex Security docs), and findings it could not validate stay in the results. A Codex Security export goes into Konvu as a vulnerability report, and Konvu hands the fix to Codex with the proof. Teams with more findings than fix capacity run both.

The question each one answers

OpenAI and Konvu agree on where discovery stops. The Codex Security FAQ says the product "accelerates review and helps rank findings, but it does not replace code-level validation, exploitability checks, or human threat assessment" (FAQ). OpenAI's Patch the Planet announcement adds: "AI is accelerating vulnerability discovery, but discovery alone does not protect users" (OpenAI).

Codex Security: what is wrong in this code?

"Codex uses repository context to identify likely vulnerabilities and validates issues in an isolated environment when possible" (docs). A cloud scan builds a threat model, reviews the repository, tries to reproduce likely findings in a sandbox, and proposes patches. The output is a ranked list of findings, some marked validated.

In the 30 days before its March 2026 launch, OpenAI says Codex Security scanned "more than 1.2 million commits" and identified "792 critical findings and 10,561 high-severity findings" (OpenAI). Each one is a triage decision for someone.

Konvu: which reported findings can an attacker use?

Konvu is not a scanner. It starts from findings produced by Snyk, Semgrep, Wiz, Dependabot, bug bounty researchers, pentesters and harnesses such as Codex Security. For each one it answers whether an attacker can use it, proves it with a working exploit, and automates the remediation (What is Konvu).

Everything OpenAI offers for security

As of October 13, 2026. OpenAI ships changes monthly.

No dependency scanner is documented as shipped as of October 13, 2026. An open pull request adds one, and says its static assessments "do not establish exploitability" (PR #1136).

Side by side

How Codex Security decides a finding is real

In Codex cloud. "Validation tries to reproduce likely vulnerabilities in a sandbox to reduce false positives." Codex Security "runs analysis in an ephemeral, isolated container and temporarily clones the target repository." Then: "For findings with verification steps, it runs commands or tests in the sandbox and attaches the results as evidence" (FAQ). With an environment tailored to your project, OpenAI says it "can validate potential issues directly in the context of the running system" (OpenAI). That running system is the Codex cloud container you configure.

The outcome: "Findings that successfully reproduce are marked as validated". If validation fails, "The finding remains unvalidated. Logs and reports still capture what was attempted".

On your machine or in CI. The plugin's validation skill works down a ladder from a crashing proof of concept to "the smallest focused test", and ends at "code understanding: if dynamic reproduction is not feasible or proportionate after bounded attempts". The standard scan instructions: "Independently validate each unique finding against local source once", and "do not require proof of a real deployment or runtime reproduction" (core scan). A local finding can be validated from source alone.

On findings from other tools. Since June 2026, Codex Security triages "SARIF results, a CVE or GHSA, an advisory, a scanner ticket, a bug bounty report", plus Jira, Linear and GitHub alerts. This is the closest any OpenAI tool gets to Konvu's job. OpenAI calls it "a read-only static analysis: Codex treats each finding as an unproven claim and inspects repository evidence without executing the code" (triage docs). Each finding comes back confirmed, not_actionable or needs_review. A runtime check is a separate run you start.

What that adds up to. In OpenAI's docs, validation runs "when possible", and a finding that fails it "remains unvalidated". We found no published figure for how often findings are validated by execution (checked October 13, 2026). That design fits a discovery tool. OpenAI's validation skill says "Missing internal runtime setup is not suppression evidence", and Konvu agrees: a lab that cannot build the conditions should never clear a finding. Codex Security uses validation to raise confidence. Konvu uses the exploit attempt to produce the verdict.

Real bugs, rated by context the code doesn't hold

The findings are often real bugs. Who can reach them, and under which configuration, is a separate question.

OpenAI's triage verdicts. Codex Security returns needs_review when "Repository evidence isn't enough to decide because required information is missing, ambiguous, runtime-dependent, environment-dependent, or policy-dependent" (triage docs, October 2026). Those findings go to a person because the repository cannot settle them. Konvu's lab is built for them.

Patch the Planet (June 22, 2026). OpenAI writes that frontier models "produce a high volume of false positives that can contribute to the already overwhelming backlog maintainers are facing." Its answer is Trail of Bits researchers who "reproduce the evidence, check findings against project-specific documentation and threat models" (OpenAI). Trail of Bits: "Without explicit guidance, models default to rating everything as critical." And: "The advantage is no longer in finding bugs, but everything after" (Trail of Bits). OpenAI's partner is describing the work Konvu automates.

GOGS, CVE-2026-25242 (February 2026). OpenAI credits it to Codex Security. "When the global RequireSigninView setting is disabled (default), any remote user can upload arbitrary files to the server via /releases/attachments and /issues/attachments" (CVE record, CVSS 4.0 6.9 MEDIUM). The finding is right. Whether a given instance is exposed comes down to one setting in the deployment.

Codex Security's beta (March 2026). OpenAI cut "the rate of findings with over-reported severity by more than 90%" (its figure, no baseline published). The same post: "Context is essential when evaluating real security risks" (OpenAI). We agree.

What these tools get right. They find serious bugs. OpenAI Security Research found what GnuPG called a "critical security bug" in gpg-agent (GnuPG, January 2026). Daniel Stenberg credits AISLE, Zeropath and Codex Security with "somewhere between two and three hundred bugfixes merged in curl" and "Probably a dozen or more" CVEs (curl). In Patch the Planet, GPT-5.5-Cyber generated "24 local privilege escalation exploits" for Linux (OpenAI). This page is about what happens after the finding.

How Konvu decides

Every report gets an exploit attempt. A Codex Security export enters Konvu as a vulnerability report, like a bug bounty submission or a pentest: "Konvu handles every report the same way, whether a person or an AI tool wrote it: it is triaged, deduplicated, and reproduced against your code" (External reports). Konvu splits an export into one report per independently fixable finding, triages each against your program's scope and rules, links duplicates, and runs the exploit on the rest (Vulnerability reports), automatically as reports arrive or on request.

The lab. "Nothing runs against your infrastructure. Konvu builds and deploys its own copy of your application from your repository." It deploys the version the report names and smoke-tests it before any attack. Labs run in a dedicated AWS account used for nothing else, with the application and the attacker on separate hosts, attacker egress denied by default, and teardown at the verdict (How reproduction works, pipeline write-up).

The verdict. Konvu breaks the report into claims, such as authentication, tenant isolation and data exposure, and marks each proven, disproven or inconclusive. "The rollup follows fixed rules that no model can override." The result is one of four (Reading a reproduction):

• Exploitable, shown as Reproduced: the exploit fired and its impact was observed.
• Not exploitable: the vulnerability is not there, or the report does not match the code. A disproof investigation lists what was checked.
• Hardening: the weakness is real in the code, but a mitigation stops it from being exploited at runtime. Worth fixing, at a lower priority.
• Inconclusive: the lab could not reach a result, and the run carries its reason, usually an environment limitation such as a managed cloud service or real credentials. Konvu's docs say it "does not mean the finding is safe."

Each result lists Where the lab differs from production, so you can judge whether a difference matters.

Scanner findings. For dependency findings, Konvu checks four preconditions, each Met, Not met or Inconclusive with evidence: the dependency is installed, the environment matches, the vulnerable code is reachable, and an attacker controls input that reaches it (Reading an assessment). Usage often decides severity: OpenSSL rated 10 of 12 AI-found CVEs Low in January 2026, noting for one that "It is uncommon to accept untrusted PKCS#12 files in applications" (OpenSSL). Konvu also reproduces scanner SAST and SCA findings.

The fix. Konvu automates the remediation: draft PRs that run your tests for scanner SCA and SAST findings, and a Copy fix prompt with the proof for Codex or Claude Code on reports. Then it re-runs the exploit against the fix to confirm it no longer fires.

Models. Konvu runs on OpenAI and Anthropic models, and we benchmark them continuously to use the best one for each task. The model is one component (why).

One run. Our pipeline write-up covers Grafana CVE-2024-1313, where an authenticated user in one organization can delete another organization's snapshot. Konvu built the affected version and confirmed the bug on the first attempt: Exploitable, 47 tool calls, $0.44 of model calls, 17 minutes 40 seconds. That is one run on a simple bug. We have not published an average cost or success rate.

Where Codex Security is stronger

• Discovery. It finds new vulnerabilities in your code, and OpenAI reports "Fourteen CVEs have been assigned" from its open-source work (OpenAI). Konvu finds nothing on its own.
• Open source and CI. The CLI and SDK are Apache-2.0, with recipes for GitHub Actions, GitLab CI/CD, Azure Pipelines and Jenkins.
• Scans you can steer. An editable threat model, SECURITY.md policies and recorded false-positive reasons guide what later scans look for.
• A regression test with each patch. When testing is safe and practical, each patch comes with "a focused regression test that fails before the fix and passes after it" (fix findings).
• Scans before merge. Diff scans, a pre-commit hook and PR review find new vulnerabilities before code lands.
• Deep scans. Up to 40 full scans across parallel workers, for as long as 96 hours, which OpenAI says "can reduce variability" (deep scans).
• Free credits. $500 in Cloud scanning credits per eligible account.

Konvu's limits

• Not a scanner. Konvu needs findings from Codex Security, a scanner, a researcher or a pentest.
• Bug classes outside the lab. Race conditions that need precise timing, hardware-dependent bugs, and chains that need a second system (bug bounty triage).
• The lab is a copy. Paid features, managed cloud services and real credentials can't always be rebuilt. Those runs end Inconclusive.
• No published accuracy benchmark.

Running Konvu and Codex Security together

Codex Security answers code-local questions, such as whether a diff introduces a vulnerability. Konvu is the decision layer for the whole backlog, where Codex Security findings sit in one record next to Snyk, Semgrep, Wiz and bug bounty findings.

1. Codex Security finds. In Cloud, the plugin, CI, or on a pull request.

2. Export. findings.json or SARIF (export docs).

3. Upload to Konvu. In the dashboard under Triage > Vulnerability reports > Upload report, or from CI on Enterprise (CLI):

   konvu finding vulnerability-report submit --repo github:acme/web --source codex-security --file findings.sarif
   
   

   With automated triage on, Konvu splits, triages, dedupes and reproduces. A custom program takes your scope and rules in Markdown.

4. Fix in Codex. Copy fix copies a prompt with the vulnerability, the proof and the recommendations. Secrets in the evidence are redacted, and report content is marked untrusted.

5. Konvu re-tests the fix. It confirms the exploit no longer fires on the fix branch.

6. Close the loop in Codex Security. Mark what Konvu ruled out as a false positive there, with the reason, so later scans remember it.

Guardrails covers the step before: it checks Codex's changes against your security guidelines while it writes code.

Pricing models

Codex Security bills model tokens. Konvu bills verdicts.

Sources: Codex Security FAQ, CLI reference and konvu.com/pricing. Model costs are inside Konvu's price. Inconclusive verdicts and failed fix PRs are free.

Sources checked

All sources checked on October 13, 2026.

• OpenAI: Codex Security docs, FAQ, Setup, Triage backlog, Fix findings, Deep scans, Export findings, Plugin changelog, CLI, CLI FAQ, CLI reference, Code Review, GitHub integration, Security Review, Plugin quickstart, Pricing, Help center, Plugin page, codex-security repo, PR #1136, Aardvark, Research preview post, Trusted Access for Cyber, Patch the Planet, Expanding Daybreak, Daybreak
• Third parties: Trail of Bits post, CVE-2026-25242, OpenSSL advisory, GnuPG announcements, Mythos finds a curl vulnerability
• Konvu: Pricing, Codex integration, Bug bounty triage, Guardrails, What is Konvu, External reports, Vulnerability reports, Triage, How reproduction works, Reading a reproduction, Reading an assessment, Dismissing false positives, Assessment settings, CLI

Related comparisons

• Konvu vs Claude Security: Anthropic's security tools next to Konvu.
• Why can't we just use Claude for this?: What a frontier model alone misses on triage.
• From vulnerability finding to exploit proof: The reproduction pipeline and the Grafana run.
• Mythos made finding free: Why triage is the bottleneck now.
• Codex integration: Konvu's MCP server, CLI and Guardrails in Codex.
• Bug bounty triage: The same pipeline on researcher reports.