Code security
Authentication, authorization, API boundaries, injection patterns, SSRF, CORS, JWT, and unsafe file handling.
- auth gaps
- authorization/RBAC issues
- SQL injection patterns
- SSRF-prone fetching
- open CORS
Security review for AI-assisted software development
Secure the Code Your Team Ships With AI
We review repositories for security risks introduced by AI-generated code, LLM applications, prompts, agents, RAG pipelines, MCP tools, secrets, dependencies, containers, and infrastructure.
AI Security Repo Audit
Repository security scan
Dependency vulnerability scan
Secret detection
Docker/container review
Infrastructure-as-code review
AI/LLM usage review
Prompt-injection risk review
Problem
AI-assisted development changes the security review.
Teams are shipping faster with Cursor, Claude, ChatGPT, Copilot, and other AI coding tools. That speed is useful, but it also creates risks that generic scanners can miss.
Insecure AI-generated code
Hardcoded secrets and API keys
Prompt injection vulnerabilities
Unsafe RAG ingestion pipelines
Overpowered AI agents
Dangerous MCP tools
Sensitive data sent to external LLM providers
Misconfigured cloud/IaC resources
Vulnerable dependencies
What we review
A repo audit combines traditional AppSec coverage with an AI-specific review layer for LLM apps, prompts, RAG, agents, and MCP tools.
Secrets
API keys, service tokens, credentials, exposed environment variables, and secrets embedded in prompts or config.
- API keys
- database credentials
- cloud tokens
- committed .env files
- secrets inside prompts
Dependencies
Package vulnerabilities, risky lockfiles, stale libraries, supply-chain exposure, and unsafe dependency patterns.
- package vulnerabilities
- lockfile risks
- stale AI SDKs
- unsafe transitive packages
- supply-chain exposure
Containers
Docker and container configuration issues that can expand the blast radius of an AI application.
- containers running as root
- exposed ports
- unsafe images
- missing resource limits
- broad runtime privileges
Infrastructure-as-code
Terraform, Kubernetes, cloud storage, IAM, service accounts, public endpoints, and encryption configuration.
- public buckets
- overly permissive IAM
- Kubernetes misconfigurations
- missing encryption
- broad service accounts
LLM usage
Model calls, provider data flow, prompt/response logging, output validation, and sensitive data exposure.
- external LLM data leakage
- sensitive prompt logs
- missing output validation
- unbounded token usage
- provider credential exposure
Prompt templates
Prompt injection exposure, system prompt leakage, unsafe template composition, and missing user-input boundaries.
- prompt injection risk
- system prompt leakage
- user input in privileged prompts
- missing prompt boundaries
- secrets in templates
RAG pipelines
Document ingestion, retrieval trust boundaries, tenant isolation, vector database exposure, and prompt injection in documents.
- unsafe ingestion
- public vector DBs
- cross-tenant retrieval
- untrusted document instructions
- missing document sanitization
AI agents
Tool permissions, filesystem/database access, approval gates, audit logs, production actions, and loop controls.
- unrestricted filesystem access
- shell access
- production API access
- missing human approval
- unbounded tool loops
MCP tools
MCP tool definitions, authorization, allowlists, mutation risk, audit logging, and sensitive action guardrails.
- dangerous tool definitions
- broad data access
- missing allowlists
- insufficient authorization
- mutating tools without guardrails
Main offer
AI Security Repo Audit
A focused security review for repositories built with AI-assisted coding tools, LLM integrations, RAG pipelines, agents, MCP tools, and cloud infrastructure.
- Repository security scan
- Dependency vulnerability scan
- Secret detection
- Docker/container review
- Infrastructure-as-code review
- AI/LLM usage review
- Prompt-injection risk review
- RAG/agent/MCP risk review
- Prioritized findings report
- Risk score
- Remediation recommendations
- 60-minute review call
Why traditional scanners are not enough
Traditional AppSec tools are useful. They can catch dependencies, secrets, and common code patterns. They do not fully understand AI-assisted development workflows, prompt templates, agent permissions, RAG ingestion, MCP tools, or LLM data flows.
The audit adds context.
We review how code, prompts, tools, retrieval, external model calls, credentials, and infrastructure fit together so findings are ranked by practical risk, not just scanner output.
Audit process
- 1
Connect or share repo
Provide scoped repository access, a code export, or a local review workflow for sensitive repositories.
- 2
We scan code and AI flows
We review code, dependencies, secrets, IaC, containers, LLM usage, prompts, RAG pipelines, agents, and MCP tools.
- 3
You receive a prioritized report
Findings are ranked by severity, exploitability, data exposure, and business impact.
- 4
We review remediation together
A 60-minute review call turns findings into practical engineering next steps.
Built for trust and practical remediation
- Practical findings, not noisy reports
- Developer-friendly remediation
- AI-specific review layer
- Clear severity ranking
- Designed for startups and fast-moving teams
Phase 1 site links
Explore the core audit offer, pricing, sample report, and resources.
Ready to review your AI-generated code before it ships?
Start with a repo-level review focused on the highest-risk code, AI, dependency, secret, and infrastructure issues.