InfoSecPro.com
AI SECURITY LAB • LLM / GenAI / ML
RED TEAM READY Application Security & Penetration Testing for Generative AI

Break your LLM app before attackers do.

InfoSecPro AI Security Lab helps startups ship GenAI features with confidence. We test LLM apps, agents, RAG, APIs, cloud—then deliver fixes your engineers can implement fast.

Request a scope Read Lab Notes What you’ll receive

Built for speed: tight scope, exploit-backed evidence, engineer-first remediation, and a retest pass for critical/high fixes.

Lab services

Pick focused testing or go end-to-end.

LLM App & Agent Pen Test

Adversarial testing for copilots, chat apps, and tool-using agents.

  • Prompt injection, jailbreaks, instruction hierarchy abuse
  • Tool misuse (function calling): unsafe args, SSRF, command paths
  • Session/memory leakage and cross-tenant boundaries
  • Abuse controls: rate limits, quotas, cost-explosion vectors

RAG / Vector DB Security

Hardening retrieval pipelines to prevent data leakage.

  • Tenant isolation, filtering, metadata leakage, doc harvesting
  • Ingestion attacks: malicious docs, prompt payloads, poisoning
  • Access control patterns for sensitive corpora
  • Secure indexing and retrieval validation

ML Pipeline & Model Supply Chain

Threat model and test training/serving pipelines and dependencies.

  • Data poisoning controls & dataset provenance
  • Artifact signing, SBOM, dependency and container risks
  • Hosting security, secrets, IAM, and network boundaries
  • Monitoring for abuse, drift, and sensitive output patterns

AI Threat Modeling (Fast)

A crisp threat model and test plan for a specific workflow.

  • Trust boundaries + abuse cases aligned to OWASP LLM risks
  • Security requirements + acceptance criteria
  • “Fix-first” control guidance for your stack

Security Enablement for AI Shipping

Make security repeatable: checks, gates, and playbooks.

  • Pre-release AI security checklist + CI/CD gates
  • Logging & detection inputs for LLM abuse
  • Developer training: secure prompting, RAG hygiene, tool safety

Attack surface coverage

We test what attackers actually chain together.

Prompts & UI

  • System/developer prompt exposure & override paths
  • Context manipulation, hidden instructions, encoding tricks
  • Unsafe rendering: markdown/HTML injection, link abuse
  • Policy bypass reliability & failure modes

Tools, Agents, Actions

  • Function calling validation, allowlists, schema enforcement
  • Authorization gaps (IDOR), cross-tenant data paths
  • SSRF via connectors, internal endpoints, file access
  • State/memory: leakage across users and sessions

RAG & Knowledge Bases

  • Vector store isolation + metadata filtering correctness
  • Ingestion threats: malicious docs, prompt payloads, poisoning
  • Extraction tests: sensitive doc enumeration & reconstruction
  • Evaluation gates: retrieval correctness vs. security

APIs & Web App

  • Authn/authz, sessions, tokens, scopes
  • Injection, SSRF, deserialization, upload risks
  • Abuse prevention: rate limiting, WAF patterns

Cloud & Platform

  • IAM misconfigurations, network segmentation, egress controls
  • Storage security, KMS usage, audit trails
  • Runtime hardening (containers/serverless)

Detection & Response

  • LLM abuse telemetry: prompt/tool traces, anomalies
  • Data exfil indicators and alerting guidance
  • AI incident response playbooks and runbooks

Lab method

Threat-model driven, exploit-backed, remediation-focused.

1) Map & plan

We diagram your system and define the highest-risk chains.

  • Trust boundaries: user → app → LLM → tools → data
  • Test plan based on your workflows and business risk

2) Attack

Hands-on testing with reproducible evidence.

  • Prompt injection & tool abuse chains
  • RAG leakage and poisoning tests
  • API + cloud misconfig exploitation paths

3) Fix & verify

We ship patches and retest critical/high items.

  • Remediation workshop + code patterns
  • Re-test and closure evidence
  • Security backlog + lightweight roadmap

Outputs

Clear, prioritized, ready for engineering execution.

Executive brief

  • Top risks in plain language (investor/customer-friendly)
  • Impact + likelihood with recommended next steps
  • Optional SOC 2 / ISO-ready security narrative

Technical report

  • Repro steps, payloads, and evidence
  • Exploit chains for complex issues
  • Fix guidance with code-level examples

Remediation backlog

  • Tickets formatted for Jira/GitHub
  • Severity + effort estimates
  • Quick wins vs. structural improvements

Retest notes

  • Verification results for fixes applied
  • Residual risk and compensating controls
  • Regression test suggestions

Contact the lab

No backend: the form opens your email client with a prefilled message.

Direct

Email: Tibi@infosecpro.com
Phone (US): 732-763-2814

Fast scoping checklist
  • LLM provider(s): OpenAI / Anthropic / Azure / self-hosted
  • Agent/tools: what actions can it take?
  • RAG: vector DB + doc sources + sensitivity
  • Auth: SSO/OAuth + tenant model
  • Cloud: AWS/Azure/GCP + CI/CD
  • Deadline: launch date or customer request

Send a request

Read Lab Notes

If the button doesn’t open an email client, just email Tibi@infosecpro.com.