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Supported AI models on Workik
GPT 5 Mini, GPT 5, GPT 4.1 Mini, GPT o4 Mini, GPT o3
Gemini 2.5 Flash Preview, Gemini 2.0 Flash, Gemini 1.5 Pro
Claude 4 Sonnet, Claude 3.5 Haiku, Claude 3.7 Sonnet
Deepseek Reasoner, Deepseek Chat, Deepseek R1(High)
Llama 4 Maverick 17B Instruct, Llama 3.3 70B, Llama 3.1 405B Instruct
Pixtral Large, Mistral 8x7B Instruct, Mistral Small, Mistral Large, Codestral
Models availability might vary based on your plan on Workik
Features
Scan Entire Codebase
Analyze complete repositories, monorepos, & microservices to detect security flaws across files or modules.
Audit Legacy Codebases
Detect and Apply fixes suggested by AI for outdated crypto, unsafe patterns, and injection risks in legacy codebases.
Secure CI Builds
Automatically block builds in CI/CD when critical vulnerabilities exceed defined severity thresholds
Export Detailed Scan Reports
Generate shareable HTML, JSON, or Markdown reports for security reviews, audits, or compliance checks.
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TESTIMONIALS
Real Stories, Real Results with Workik
Workik AI saved me during a review. It flagged a logic flaw I completely missed in a critical route.
Rahul Deshmukh
Python Developer
Thank God I used Workik AI to check all our PRs, it caught a broken access bug right before the merge!
Meera Iyer
Application Security Analyst
Workik AI saved us big time. It scanned our legacy repo and flagged an exposed AWS key hidden deep in config.
Martin Santiago
Security Engineer
What are the most common use cases of Workik AI-Powered Code Vulnerability Scanner for developers?
Workik AI helps developers detect security risks early and ship safer code. Common use cases include but are not limited to:
- Scan login flows for missing access checks or weak session handling
- Detect hardcoded secret leaks like API keys, AWS credentials, or tokens in config files
- Block PRs with risky code patterns like eval(), exec(), or raw SQL
- Review third-party packages or open-source code for vulnerable or unsafe usage
- Scan Dockerized and serverless code for exposed env vars or insecure defaults
- Audit legacy projects for deprecated crypto, file upload flaws, or unsanitized input
- Run pre-merge scans on services inside monorepos or specific API stacks
What type of context can I set for better vulnerability scanning in Workik AI?
Adding context is important for Workik AI to detect vulnerabilities and provide tailored fix suggestions for your codebase. You can set the context by:
- Connecting GitHub, GitLab, or Bitbucket repositories
- Selecting specific folders or services to scan, like /auth or /payments
- Uploading individual files or directories manually
- Including config files like .env, docker-compose.yml, or package.json
- Scanning pull requests or commit diffs to focus on recent code changes
Can Workik AI’s Code Vulnerability Scanner detect leaked confidential data, and misconfigurations in codebases?
Yes. Workik AI scans .env, config files, and source code to detect exposed confidential data, credentials, tokens, and misconfigurations. It flags issues like hardcoded AWS keys in frontend files or Firebase tokens in backend routes and suggests secure handling using environment variables or secret managers.
Do I need to configure rulesets or write policies for the Vulnerability Scans?
Nope. Workik AI requires zero manual configuration. It understands project context, coding conventions, and best practices out of the box. However, for advanced users, you can set custom rules (e.g., reject commits exposing .env files or using weak hashing algorithms like MD5).
How does Workik AI Code Vulnerability Scanner integrate into our existing DevSecOps pipeline?
You can plug Workik AI-powered Code Vulnerability Scanner into your CI/CD workflows using GitHub Actions, GitLab CI, or Bitbucket Pipelines. Run vulnerability scans on every push, block merges when critical vulnerabilities are found, and trigger scans on open pull requests. It also goes further by providing AI-enhanced remediation paths and tighter integration with your repo context.
Can Workik AI-powered Code Vulnerability Scanner help with legacy systems or monolithic applications?
Yes. Workik AI can quickly scan large, older codebases to find security issues that manual reviews often miss. It detects things like outdated libraries, weak encryption, missing input checks, and insecure patterns, even in messy or undocumented code.
How is Workik better than linters or basic SAST tools?
While linters enforce style and some security rules, Workik AI understands code semantics, not just syntax. It tracks data flows, understands control structures, and flags issues traditional SAST tools miss—like logic flaws, implicit trust boundaries, or unsafe chaining in async workflows.
Can Workik AI’s Code Vulnerability Scanner help with scanning open-source or third-party dependencies?
Yes. Workik AI can scan external codebases before you adopt them—flagging risky functions, outdated libraries, insecure patterns, and misconfigured defaults.
Does Workik AI support scoped scanning in monorepos or modular architectures?
Absolutely. Workik AI lets you scope scans to specific folders, services, or modules which is ideal for monorepos and multi-service architectures. It helps teams run targeted scans without reprocessing the entire codebase.
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