CodeSec AI-Fixing Agent generates remediation guidance across the full stack: application code patches, network device configuration fixes, OS hardening, Windows registry updates, SSL / TLS protocol and cipher hardening, web server and firewall rule fixes. Every fix is reviewed by a Codesecure engineer before delivery.
CodeSec AI-Fixing is an AI-assisted remediation agent that turns confirmed vulnerability findings into concrete, applicable fixes across the full stack. Application findings get framework-specific code patches. Network findings get device configuration snippets (Cisco IOS, Palo Alto, Fortinet, Juniper). Windows findings get registry update commands and Group Policy guidance. Linux findings get sysctl, PAM and service hardening. SSL / TLS findings get exact protocol-disable and cipher-string commands for nginx, Apache, IIS, F5, HAProxy.
We deliberately position AI-Fixing as a reviewed-output capability, not a fully autonomous applier. Our engineer reviews every fix before it reaches your team. This keeps the false-fix rate low, avoids breaking changes, and respects the realities of how production systems actually evolve. The output is something your engineer can apply in minutes with copy-paste-and-verify, not a black box.
Most vulnerability programmes fail at remediation, not detection. Findings pile up because the fix is not obvious to the engineer who owns the asset, or because the right command for that specific vendor / version is hard to find. A developer might know how to fix SQL injection but not the exact ssl_protocols line for nginx; a network engineer might know how to harden a firewall but not the exact registry key to disable TLS 1.0 on Windows Server 2019. The result is open-finding bloat that hurts audit posture and creates real risk.
AI is genuinely useful here because most vulnerability classes have well-known fix patterns: parameterised queries, output encoding, secure deserialisation, framework-specific input validation, disable-SSLv3-cipher strings, registry hardening keys, ACL templates, Group Policy paths, CIS-benchmark setting numbers. An LLM with the finding context and a curated fix catalogue produces the exact command or config snippet for your specific platform faster than searching vendor docs. The engineer review step makes sure the draft is actually safe.
CodeSec AI-Fixing covers application, network and system-layer remediation with platform-specific output:
45-minute walkthrough call with our AI team. We will show you the AI-Fixing workflow on a sample finding, demonstrate the engineer-review step and discuss applicability to your codebase and tech stack. Instant response, no delay.
Request Early AccessAI-Fixing runs as a structured remediation workflow with engineer oversight, identical shape for application and network / system findings:
Confirmed findings ingested with metadata: layer (app / network / OS), target platform, version, exact misconfiguration or vulnerable element.
For app findings: surrounding code, framework version, test coverage. For network / system: device vendor, OS version, current config snippet, CIS benchmark relevance.
AI drafts platform-specific fix output: code diff for apps, nginx / Apache config block for TLS, registry .reg file for Windows, sysctl line for Linux, ACL template for firewalls.
Codesecure engineer reviews each candidate for correctness, platform-specific syntax, breaking-change risk and verification approach.
Reviewed fixes delivered as a ready-to-apply pack: code diffs, config snippets, registry scripts, verification commands. Optional post-fix retest during scheduled engagement.
Every AI-Fixing engagement ships with a ready-to-apply remediation pack covering whichever layers your findings span:
// AI Stack & Integrations
30-minute call with our AI engineering lead. Discuss your tech stack, finding backlog and remediation workflow integration with no sales pressure.
Talk to the AI TeamBoth. AI-Fixing covers the full stack: application code (framework-specific patches), web server configs (nginx / Apache / IIS), SSL / TLS protocol and cipher hardening, Windows registry updates and Group Policy, Linux OS hardening (sysctl / PAM / SSH), firewall / switch / router configs (Cisco / Palo Alto / Fortinet / Juniper), Active Directory hardening, cloud misconfig fixes (AWS / Azure / GCP), and CIS-benchmark-keyed settings. A pentest typically produces findings across multiple layers; AI-Fixing produces remediation for each layer in the format the owning engineer can apply directly.
For a finding like "SSL 3.0 / TLS 1.0 / TLS 1.1 enabled" or "weak ciphers offered (RC4, 3DES)", AI-Fixing outputs the exact remediation for your target platform. nginx: a corrected ssl_protocols TLSv1.2 TLSv1.3; and ssl_ciphers line. Apache: SSLProtocol and SSLCipherSuite directives. IIS / Windows Server: the registry .reg snippet under HKLM\\SYSTEM\\CurrentControlSet\\Control\\SecurityProviders\\SCHANNEL\\Protocols with the correct DWORD values to disable each protocol. F5 / HAProxy / nginx-ingress: vendor-specific syntax. Plus the verification command (openssl s_client / nmap --script ssl-enum-ciphers) so your engineer can confirm the fix worked.
No. AI-Fixing delivers reviewed remediation packs (code diffs, config snippets, registry scripts, ACL templates, verification commands) that your team applies through your normal change-management process. We do not auto-apply into production. The human approval step at your end is intentional and preserves your engineering accountability.
Application: JavaScript / TypeScript (Express, Next.js, NestJS), Python (Django, Flask, FastAPI), Java (Spring), C# / .NET, PHP (Laravel, Symfony), Ruby (Rails). Web servers: nginx, Apache, IIS, F5, HAProxy. Network: Cisco IOS / ASA, Palo Alto PAN-OS, Fortinet FortiGate, Juniper Junos, pfSense. OS: Windows Server 2016/2019/2022, RHEL / CentOS / Rocky / Alma, Ubuntu / Debian. Cloud: AWS, Azure, GCP, Kubernetes. CIS benchmarks: Windows, Linux, Docker, Kubernetes, Azure, AWS, Cisco. Coverage for older / niche platforms is best-effort and we are honest about confidence level during scoping.
Engineer review catches most regressions before delivery. For config / network / OS fixes we include verification commands and where applicable a rollback snippet. For app code fixes we recommend unit-test additions and rely on your CI / QA gate as the final safety net. Disabling old TLS versions or weak ciphers can break very old clients (Windows XP, Android 4, legacy IoT) so we surface compatibility trade-offs in the rationale text. We do not promise zero regression risk on any change.
We are deliberate about this. Sensitive source code, configs and finding data stay within Codesecure infrastructure during processing. For LLM-required reasoning steps, we evaluate enterprise commercial LLM APIs with enterprise-grade data handling (no training on customer data), and self-hosted open-weights options for the most sensitive engagements. We scope your engagement to your data-handling preference.
Less well. AI-Fixing is strongest for well-defined vulnerability classes with known fix patterns at code, config, registry or device-rule level (SQLi, XSS, TLS hardening, registry keys, ACLs, IAM policy, CIS benchmark items, etc.). For architectural findings (multi-tenancy isolation, identity model redesign, network re-segmentation, supply-chain hardening) the consultant handles the recommendation directly and AI plays a smaller drafting role.
Currently available as an augmentation to Codesecure pentest and source-code-review engagements rather than as a standalone service. Pricing is folded into the engagement scope rather than billed separately. We are scoping a standalone retainer model for clients with large finding backlogs.
Instant response, no delay. We respond within an hour during business hours, send a scoped engagement proposal in 24-48 hours under NDA, and AI-Fixing augmentation is added to the engagement timeline where it fits.
CodeSec AI-Fixing accelerates remediation across application, network, OS, TLS / cipher, registry and cloud configuration findings. Engineer-reviewed remediation packs ready to apply, lower mean-time-to-fix, audit-ready closure evidence. Request early access for your team.
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