Aikido Security

Performs static code analysis across multiple programming languages by parsing source code into abstract syntax trees (AST) and pattern-matching against vulnerability signatures. The system scans repositories without executing code, identifying injection flaws, hardcoded secrets, unsafe API usage, and logic errors. Results are returned within 30 seconds for typical codebases by leveraging incremental scanning and caching of previously analyzed files.

Scans open-source dependencies declared in package managers (npm, pip, Maven, Go modules, etc.) and matches them against a continuously-updated CVE database to identify known vulnerabilities. Generates Software Bill of Materials (SBOM) in standard formats, tracks dependency versions, and identifies outdated packages. The system performs transitive dependency analysis to detect vulnerabilities in indirect dependencies that may not be explicitly declared.

Deploys an in-application firewall (Zen) that monitors and blocks injection attacks (SQL injection, command injection, etc.) and enforces rate limiting at runtime. The firewall instruments the application to intercept dangerous operations (database queries, system commands, etc.), validates inputs against attack patterns, and blocks or logs suspicious requests. This provides runtime protection for vulnerabilities that may not be caught by static or dynamic testing.

Detects and blocks bot traffic and API abuse by analyzing request patterns, behavioral signatures, and anomalies. The system identifies automated attacks (credential stuffing, account enumeration, scraping, DDoS) by recognizing patterns like identical requests from different IPs, rapid-fire requests from single sources, and requests that deviate from normal user behavior. Blocking can be enforced through rate limiting, CAPTCHA challenges, or request rejection.

Integrates Aikido scanning into CI/CD pipelines to automatically scan code, dependencies, and infrastructure on every commit or pull request. The integration includes policy enforcement gates that block merges if findings exceed configured thresholds, automated remediation through pull request creation, and detailed scan reports in CI/CD logs. Supports GitHub Actions, GitLab CI, Jenkins, and other CI/CD platforms through webhooks and API integrations.

Provides IDE plugins (VS Code, JetBrains IDEs, etc.) that show security vulnerabilities inline as developers write code. The plugin displays vulnerability warnings, provides quick-fix suggestions, and integrates with Aikido's AI triaging to show only relevant findings. Developers can view detailed vulnerability information, see remediation suggestions, and apply fixes directly from the IDE without leaving their development environment.

Detects malware and malicious code in source code, dependencies, and binaries using proprietary threat intelligence (Aikido Intel) combined with pattern matching and behavioral analysis. The system identifies known malware signatures, suspicious code patterns (e.g., cryptominers, backdoors, data exfiltration), and dependencies with malicious intent. Findings include threat classification, severity, and remediation guidance.

Scans open-source dependencies to identify license types and detect license compliance violations. The system maintains a database of common open-source licenses (MIT, Apache 2.0, GPL, AGPL, etc.) and flags dependencies with restrictive or incompatible licenses. Provides reports showing license distribution across the codebase and recommendations for replacing incompatible dependencies.

Analyzes Infrastructure-as-Code files (Terraform, CloudFormation, Kubernetes manifests) to detect misconfigurations, insecure defaults, and policy violations before infrastructure is deployed. The scanner parses IaC syntax, validates against built-in security policies (e.g., requiring encryption, restricting public access), and identifies deviations from compliance frameworks. Results include specific line numbers and remediation guidance for each misconfiguration.

Scans OCI-compliant container images (Docker, Podman, etc.) to identify vulnerable packages, outdated base images, and insecure configurations. The scanner extracts the image filesystem, enumerates installed packages, and matches them against CVE databases. It also analyzes image metadata (entrypoint, environment variables, exposed ports) to detect security misconfigurations. Scanning can be triggered on image push to registry or on-demand from the Aikido dashboard.

Scans source code, configuration files, and commit history to detect hardcoded secrets including API keys, passwords, certificates, encryption keys, and database credentials. Uses pattern matching and entropy analysis to identify potential secrets that may have been accidentally committed. The scanner checks both current code and historical commits to find secrets that were committed but later removed (still present in Git history).

Performs dynamic security testing of running web applications and APIs by sending crafted HTTP requests to identify runtime vulnerabilities such as injection flaws, broken authentication, insecure deserialization, and API security issues. The DAST scanner crawls the application, builds a model of endpoints and parameters, and tests each with payloads designed to trigger vulnerabilities. Results include proof-of-concept demonstrations of vulnerabilities and specific remediation guidance.

Uses machine learning models to analyze security findings and filter out false positives by evaluating exploitability context, code reachability, and actual attack surface. The system assigns risk scores based on whether a vulnerability is actually reachable in the application code, whether it requires specific preconditions to exploit, and whether the vulnerable code path is actually used. This AI triaging layer sits between raw scanner output and the developer dashboard, reducing noise by 92% according to testimonials.

Automatically generates code patches to fix detected vulnerabilities in source code, dependencies, and infrastructure configurations. The AutoFix system analyzes each vulnerability, determines the minimal code change required to remediate it, and generates a patch that can be automatically applied or reviewed before merging. For dependencies, it recommends and applies version upgrades; for code vulnerabilities, it generates refactored code; for IaC, it generates corrected configurations.

Continuously monitors cloud infrastructure (AWS, Azure, GCP) for security misconfigurations, compliance violations, and deviations from security baselines. The CSPM system connects to cloud provider APIs, enumerates resources, and evaluates them against security policies. It detects issues like overly-permissive IAM policies, unencrypted storage, exposed databases, and missing security controls. Findings are prioritized by risk and include remediation steps.

Deploys 200+ AI agents that autonomously perform penetration testing against applications and infrastructure by executing attack scenarios, validating exploitability, and generating patches. Each agent specializes in specific attack vectors (injection, authentication bypass, privilege escalation, etc.) and works in parallel to test every deployment. The system validates that exploits actually work, generates proof-of-concept code, and automatically creates patches that are retested to confirm remediation.