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Stella Redteam Operations
Now booking Q4 2026 engagements

Securing the Future of GenAI & Autonomous Workflows

Offensive security assessments, RAG auditing, and agentic workflow red teaming for enterprise AI applications.

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Advisories
27
Report SLA
5 days
Retests
Free
engagement/live-trace● REC
  • 00:01[RECON]Enumerating retrieval endpoints and tool manifests
  • 00:07[PROBE]Injecting hidden instruction into indexed PDF
  • 00:14[TRIGGER]Victim asks: "Summarise Q3 vendor contract"
  • 00:15[HIJACK]Agent invokes send_email() with attacker args
  • 00:16[IMPACT]Tenant B records exfiltrated via shared index
  • 00:21[REPORT]Finding ADV-2026-001 written, fix verified
SeverityCRITICAL

/ Services

Three layers. One attacker mindset.

We test the product that exposes your AI, the pipeline around the model, and the agents acting on your behalf, because breaches happen at the seams.

Layer 1

Application Layer Security

Surface, API & Identity Protection

Comprehensive offensive testing of the web, mobile, and API interfaces hosting your AI capabilities.

Key Capabilities

  • Web & Mobile API Penetration Testing
  • Broken Object Level Authorization (BOLA / IDOR)
  • Session Handling & OAuth Flow Verification
  • Client-Side Output Rendering & Input Sanitization
Layer 2

GenAI & RAG Layer Security

Data, Prompt & Retrieval Pipeline Auditing

Deep technical evaluation of generative models, system prompts, vector search pipelines, and knowledge bases.

Key Capabilities

  • Direct & Indirect Prompt Injection Testing
  • Vector Database Cross-Tenant Data Isolation (Pinecone, ChromaDB, Azure AI)
  • Document Ingestion Pipeline & Data Poisoning Audits
  • System Prompt Extraction & Guardrail Evasion Testing
Layer 3

Agentic AI Layer Security

Autonomous Execution & Tool-Calling Red Teaming

Adversarial red teaming for autonomous agents using decision loops, function calling, and multi-agent systems.

Key Capabilities

  • Goal Hijacking & Autonomous Instruction Overrides
  • Confused Deputy Attacks on Backend API Tools & Databases
  • Model Context Protocol (MCP) & Inter-Agent Communication Exploitation
  • Long-Term Context & Conversation Memory Poisoning

/ Engagement Packages

Productized service bundles.

Fixed-scope engagements mapped to the layers of your AI architecture. Pick the bundle that matches what you ship.

Bundle

GenAI Essentials

Ideal for knowledge-base chatbots, document summarization apps, and internal RAG search engines.

Scope Included

Application Layer + GenAI & RAG Layer

  • Full Web/API Security Review
  • RAG Pipeline & Vector DB Isolation Audit
  • Direct/Indirect Prompt Injection Testing
  • OWASP Top 10 for LLMs Deliverable Mapping
Request Scope for GenAI Essentials
Bundle

Autonomous Agent Security

Ideal for workflow automation bots, DevOps assistants, and transactional AI tools.

Scope Included

Application Layer + Agentic AI Layer

  • Full Web/API Security Review
  • Agentic Goal Hijacking & Execution Testing
  • Function Calling & Tool Abuse Exploitation
  • OWASP Top 10 for Agentic Applications (ASI) Mapping
Request Scope for Agentic Red Team
BundleMost Popular

Full-Stack AI Audit

Ideal for enterprise platforms combining multi-tenant document retrieval with autonomous agent execution.

Scope Included

All 3 Layers Combined (Application + GenAI/RAG + Agentic AI)

  • Complete End-to-End System & API Assessment
  • Deep RAG Ingestion & Vector Isolation Audit
  • Autonomous Multi-Agent Tool Hijacking Red Team
  • Comprehensive Threat Report + Executive Attestation Letter
  • OWASP LLM + OWASP Agentic + MITRE ATLAS Mappings
Request Scope for Full-Stack Audit

/ Methodology

See an attack chain, step by step.

Every finding ships with a reproducible payload, the evidence it landed, and a verified fix. Pick a scenario to walk through how we break it.

High3-step chain

  1. Plant

    Upload a benign-looking document containing a hidden retrieval-bait paragraph into Tenant A.

  2. Poison

    The embedding ranks above legitimate chunks for common queries, bleeding across the shared index.

  3. Extract

    A Tenant B user asks a routine question; the model quotes Tenant A records in its answer.

Proof of concept

query: "quarterly pricing sheet"
filter: tenant_id != caller   # bypassed
top_k: 8
→ returned chunks from tenant_id=a91f
Verified remediation

Enforce tenant filter server-side at the vector store and re-verify with namespace-scoped credentials.

/ Research

Research & Security Advisories

Technical writeups, threat intelligence, and zero-day research published by our security team.

/ Contact

Request a Proposal

Ready to test your GenAI or Agentic deployment? Get in touch with our security team.