SERVICES · FLAGSHIP

Test the model. Validate the agent.

Traditional penetration testing stops at the model boundary. Fortreum Override tests what it can't reach — the model, the agent, and the trust boundary — the way an adversary would. AI Offensive Security & Penetration Testing, with proof of exploit for every finding.

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WHY THIS MATTERS

AI breaks
the rules

AI and LLM systems are embedded in customer-facing products, internal copilots, and automated workflows — and adversaries are already targeting them. Traditional testing focuses on infrastructure and code, and stops at the model boundary. That leaves prompt injection, tool abuse, data leakage, and agent autonomy untested.

of traditional
pen testing.

TWO DIFFERENT TESTS

Test the AI itself – not just what surrounds it.

Traditional pen testing

Tests the infrastructure around the AI — network ports, web app inputs, API authentication
Looks for known CVEs, injection, and misconfiguration
Stops at the model boundary
Can't validate agent autonomy or trust violations

AI offensive security

Targets model behavior, prompt context, and outputs
Validates tool invocation and agent autonomy
Tests RAG pipelines and vector security
Maps to the OWASP LLM Top 10 and MITRE ATLAS
Delivers proof-of-exploit evidence for every finding

WHAT WE TEST

Eight attack categories. one methodology.

We probe every layer where AI introduces new risk — from the prompt to human interaction and decisions.

// 01

Prompt & context manipulation

Direct and indirect prompt injection, jailbreaks, multi-turn escalation, encoding bypasses, and cross-modal injection.

// 02

System prompt & context security

System prompt extraction, role manipulation, delimiter attacks, and incremental leakage across conversations.

// 03

Data extraction & model attacks

RAG source leakage, training-data memorization, model inversion, cross-tenant exposure, and credential extraction.

// 04

Agent & tool abuse

Unauthorized tool invocation, plugin auth bypass, autonomous action approval bypass, SSRF via tools, and memory poisoning.

// 05

Vector & embedding attacks

RAG injection via poisoned documents, vector database poisoning, embedding inversion, and retrieval relevance manipulation.

// 06

Adversarial ML & robustness

Evasion attacks, gradient-based adversarial inputs, model extraction, fine-tuning poisoning, and backdoor detection.

// 07

Content safety & IP

Guardrail bypass, brand-damaging content, trademark misuse, and code license violations.

// 08

Human trust & decision exploitation

Misleading output generation, AI authority impersonation, weaponized hallucination, and AI-assisted social engineering.

SCOPE & COVERAGE

From a single chatbot to enterprise multi-agent platforms

We adapt to your AI stack regardless of foundation model — tested across GPT-4 and GPT-5, Claude, Gemini, Llama, and custom fine-tuned or self-hosted models.

LLM-backed applications

Chatbots, virtual assistants, copilots, knowledge assistants, and embedded AI features.

Autonomous & multi-agent systems

Single agents, multi-agent orchestration, agent-to-agent communication, and autonomous workflows.

RAG pipelines

Retrieval-augmented generation with proprietary data, vector databases, and embedding stores.

Model APIs & inference endpoints

Direct inference endpoints, model APIs, and fine-tuned or self-hosted deployments.

AI tools, plugins & integrations

Tool-calling functions, third-party plugins, MCP servers, and external API integrations.

AI-mediated workflows

Automation flows where AI makes or influences decisions across SaaS, internal systems, and finance.

STANDARDS & FRAMEWORKS

Built on the standards regulators recognize

Every engagement maps to industry-recognized AI security frameworks, so findings translate directly to compliance and governance.

OWASP Agentic (ASI)

COMPANION SERVICE

AIUC-1 validation

Independent controls audit and certification for AI agent systems — the first AI agent security, safety, and reliability certification, backed by a consortium of 75+ Fortune 500 CISOs from firms including J.P. Morgan, Microsoft, Anthropic, Cisco, Goldman Sachs, and AWS. When your buyers ask whether your AI is safe to adopt, AIUC-1 gives them an independent, evidence-backed answer.

// Principles 6

Audit principles: data & privacy, security, safety, reliability, accountability, and society.

// Duration 6 weeks

Typical AIUC-1 engagement.

// Scale 3,000+

Evaluations per quarter across Fortreum audits and AIUC red teams.

ENGAGEMENT MODEL

Right-sized at every AI maturity stage

Entry — single AI application

A single chatbot, copilot, or AI feature. OWASP LLM plus foundational ATLAS coverage. Ideal for a first AI security validation. 1 to 2 weeks.

Intermediate — multiple apps or RAG

Multiple applications or a complex single deployment. Comprehensive ATLAS matrix coverage, on a quarterly or semi-annual cadence. 2 to 4 weeks.

Advanced — enterprise / multi-agent

Multi-agent and orchestration platforms, with a continuous AI red teaming program via COAST and regulatory readiness for the EU AI Act and FedRAMP AI. 4 to 6 weeks.