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
Repo-level AI AppSec
AI Security Repo Audit
Security review for AI-assisted software development, covering code, dependencies, secrets, containers, infrastructure, prompts, LLM usage, RAG pipelines, agents, and MCP tools.
What you receive
Prioritized findings, severity ratings, risk score, remediation recommendations, and a 60-minute review call.
Who it is for
- Startups building AI products
- Small to mid-sized software teams
- Agencies building AI apps for clients
- Fintech, crypto, trading, accounting, and data-heavy businesses
- Teams adopting Cursor, Claude, ChatGPT, GitHub Copilot, or other AI coding tools
What we scan
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
Common risks we find
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 you receive
- 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
Example findings
CriticalView
Exposed API key in environment config
An attacker could use the key to access third-party services, customer data, or generate unexpected usage costs.
HighView
User input passed directly into LLM system prompt
A malicious user can override privileged instructions or influence downstream tool calls.
HighView
Agent has unrestricted filesystem access
Prompt injection or tool-output manipulation could cause the agent to read or modify sensitive files.
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.
FAQ
Is this a penetration test?+
No. This is a repository and AI application security review focused on code, dependencies, secrets, IaC, containers, and AI-specific risks. It can complement a penetration test.
Do you replace tools like Snyk, Semgrep, or GitHub Advanced Security?+
No. We use and complement proven security tools while adding AI-specific review around LLM apps, prompts, RAG, agents, MCP tools, and developer workflows.
Do you need access to our private repository?+
For the deepest review, yes, but access can be scoped. For sensitive teams, we can discuss local or limited-access workflows.
What kinds of AI risks do you review?+
Prompt injection, unsafe tool use, exposed prompts, sensitive data leakage, insecure RAG ingestion, weak tenant isolation, overpowered agents, dangerous MCP tools, and missing validation around LLM outputs.
What do we receive?+
A prioritized findings report, severity ratings, risk score, remediation recommendations, and a review call.
AI Security Repo Audit
Ready to review your AI-enabled codebase?
Start with one repo. We will identify the highest-risk issues and give your team practical remediation steps.
Repo Audit
Security review for AI-assisted software development
- Repository, dependency, secret, container, and IaC review
- LLM, prompt, RAG, agent, and MCP risk review
- Prioritized report, risk score, remediation steps
- 60-minute review call