// flagship_capability

AI Penetration Testing & Red Teaming

Adversarial testing for LLMs, agents, and AI-powered products. Prompt injection, jailbreak engineering, agent red teaming, RAG and retrieval poisoning, model supply-chain review, and output-handling attacks — delivered by a senior practitioner who has tested AI systems from seed-stage products to global-enterprise platforms.

// aligned to

OWASP LLM Top 10

// aligned to

MITRE ATLAS

// aligned to

NIST AI RMF / ISO 42001

// 02 capabilities

What we test.

Eight focused attack surfaces. Every engagement is shaped to your system — chat, agent, RAG, internal copilot, multi-tenant platform — rather than run from a checklist.

Prompt injection & jailbreak engineering

Direct and indirect prompt injection across user input, retrieved documents, tool output, and multi-modal payloads. System-prompt extraction, alignment bypass, and persistent-context attacks against production guardrails.

Agent red teaming

Goal-oriented attacks on autonomous agents — tool and function abuse, planner hijacking, scratchpad manipulation, lateral pivots through MCP servers, and coercing agents into privileged actions on your real infrastructure.

RAG, retrieval & memory poisoning

Adversarial documents in vector stores, embedding-collision attacks, instruction smuggling through retrieved context, long-term memory poisoning, and exfiltration of sensitive corpora through crafted queries.

Tool & function-call abuse

Argument injection across function-calling and tool-use APIs, confused-deputy chains between connected tools, SSRF via fetched URLs, and abuse of file, code-exec, and shell tools wired into agent stacks.

Model supply-chain review

Provenance and integrity of base models, fine-tunes, LoRAs, and adapters. Hugging Face dependency review, pickle and serialization risks, poisoned-weights detection, and review of model-loading code paths.

Output handling & downstream impact

Where model output flows into your stack — XSS via rendered markdown, SQL/NoSQL injection through generated queries, deserialization, broken access control via LLM-generated authorization decisions, and data leakage in logs and traces.

Training-data & PII exfiltration

Membership inference, training-data extraction, prompt-leak chains, and disclosure of customer data through inadequate context isolation in multi-tenant deployments.

Guardrail & policy bypass

Empirical testing of moderation classifiers, policy gateways, content filters, and refusal logic — including adversarial-suffix, role-play, encoding, and obfuscation chains.

// 03 methodology

Adversarial, not academic.

We don't run a benchmark, score the model, and call it a pentest. We build a threat model of your real system, attack it manually, and chain the primitives into something a board can understand.

// 01_threat_model

We build a system-specific threat model — every model, tool, data store, identity boundary, and human-in-the-loop checkpoint — before a single payload is fired.

// 02_attack

Manual-led adversarial testing by a senior practitioner. Automated harnesses where they help, never as a substitute for expert judgment.

// 03_chain

We chain primitives into business-impact exploits — not a list of jailbreak strings, but the path from public input to production damage.

// 04_report

Findings mapped to the OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF — with concrete, framework-aware remediations your engineers can ship.

// 04 experience

Tested at every scale.

From a seed-stage AI feature pre-launch to a global-enterprise platform under regulatory scrutiny — the threat model and the deliverable change, the rigor doesn't.

// smb

Seed to Series-A

First-AI-feature startups, vertical AI products, and AI-native SaaS. Pragmatic threat modeling and a focused offensive review before launch or enterprise sales.

AI copilots in vertical SaaS
RAG over customer knowledge bases
Single-agent chat & support apps

// enterprise

Mid-market & enterprise

Production AI integrated into customer-facing products and internal workflows. Multi-team threat modeling, agent platform reviews, and red-team campaigns against deployed copilots.

Internal copilots over private data
Multi-tenant AI features in SaaS platforms
Agent platforms with tool & function calling

// global_enterprise

Global enterprise

Multi-region AI platforms, regulated industries, and multi-vendor model footprints. Adversarial testing aligned to ISO 42001, NIST AI RMF, and sector-specific obligations across financial services, healthcare, and the public sector.

Bank- and insurer-grade LLM platforms
Healthcare AI under HIPAA / HITRUST
Multi-model gateways & enterprise MCP fleets

// deliverables

What lands in your inbox.

Threat model of the AI system, its trust boundaries, and adversary objectives
Findings ranked by exploitability and business impact — not raw severity
Reproducible proof-of-concept payloads and exploit chains
Concrete remediations: prompt design, guardrails, architecture, and detection
Executive summary written for the board, technical detail written for engineers
Optional retest after remediation

// ideal_for

Built for teams shipping AI to production.

AI-native startups validating their first launch, product teams inside larger organizations putting copilots in front of customers, and enterprise platform teams operating multi-vendor model footprints under ISO 42001, NIST AI RMF, or sector-specific obligations.

// engagement

Custom-scoped per system. Half-hour scoping call, fixed price, no mystery line items.

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