LLM
penetration testing.
Offensive testing of chatbots, copilots, RAG systems, and agentic applications — OWASP LLM Top 10, MITRE ATLAS-informed, aligned to the NIST AI Risk Management Framework.
Traditional AppSec ends where LLMs begin.
LLM-powered applications invert the security model — user input steers the program, retrieved documents alter control flow, and autonomous agents take real action with real permissions. We test that surface the way adversaries already are, with payloads, chains, and agentic exploits developed by operators who live in these systems.
Coverage spans every primary LLM risk surface: prompts, retrieval, tools, fine-tuned weights, and the guardrails you built to contain them.
Every category. Manually exploited.
Direct and indirect prompt-injection across user input, retrieved documents, tool outputs, and multi-agent hand-offs.
Training-data leakage, system-prompt exfiltration, and PII / PHI exposure through retrieval and memory.
Model, adapter, LoRA, and dataset provenance — including poisoned fine-tuning data and malicious model artifacts.
Training-time and RAG-time poisoning of embeddings, retrieval corpora, and continuous-learning pipelines.
XSS, SSRF, SQLi, and RCE downstream of LLM output — including unsafe tool invocation and code-exec sinks.
Over-scoped tools, missing human-in-the-loop, unbounded cost / action budgets, and dangerous default permissions.
Extraction of guardrails, policy, and context — and the control-flow bypasses those leaks unlock.
Embedding inversion, retrieval poisoning, cross-tenant vector bleed, and metadata-filter bypass.
Hallucinated citations, confabulated APIs, and reputational / legal exposure from over-confident output.
Denial-of-wallet, token-flood, and model-availability attacks through cost and context exhaustion.
From chat to agent. Every layer.
Single-turn and multi-turn chat surfaces, enterprise copilots, and IDE / productivity integrations.
Retrieval-augmented systems — ingestion, chunking, embedding, vector store, and retrieval-time attacks.
Tool-using agents, multi-agent orchestration, and autonomous workflows operating with real permissions.
Custom model pipelines, adapters, LoRA artifacts, and training-data provenance.
Inference endpoints, batching, moderation layers, and the REST / streaming APIs wrapping the model.
Prompt-based guardrails, classifier moderation, jailbreak resistance, and red-team regression suites.
Model. Prompt. Chain. Verify.
Threat Modeling
Application-specific threat model aligned to OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF — mapped to your actual architecture.
Adversarial Prompting
Manual jailbreak, prompt-injection, and policy-bypass testing — including multilingual, multi-modal, and indirect vectors.
Systemic Exploitation
Chaining LLM flaws into real impact — data exfiltration, tool abuse, SSRF, and unauthorized action through agentic loops.
Reporting & Retest
Reproducible payloads, fix guidance, guardrail and eval recommendations, and a verified retest after remediation.
What ships.
- 01OWASP LLM Top 10 coverage matrix
- 02MITRE ATLAS-mapped adversary findings
- 03Reproducible prompt-injection / jailbreak payloads
- 04Agent and tool-abuse exploit chains
- 05Guardrail and eval-suite recommendations
- 06Verified retest after remediation
Test the surface
your adversary wants most.
Pre-launch assessment, continuous red-team on release, or guardrail / eval design. Scoped under NDA.