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AI Security
Assessment

Adversarial testing of LLM applications, RAG pipelines, and autonomous AI agents — covering prompt injection, jailbreaking, model extraction, and the full OWASP LLM Top 10 attack surface.

StandardsOWASP LLM Top 10, ATLAS
Duration1–3 Weeks
ReportExecutive + Technical
// OVERVIEW

What is AI Security Assessment?

As AI and LLM-powered systems are rapidly deployed across industries, they introduce entirely new attack surfaces that traditional security testing cannot address. Our AI Security Assessment evaluates the security of your AI/ML systems, LLM applications, RAG pipelines, and AI agents against emerging attack vectors specific to intelligent systems — vectors that sit entirely outside the scope of conventional pen testing.

We test for prompt injection, jailbreaking, model extraction, adversarial inputs, training data poisoning, and insecure plugin and tool use. Our assessors combine expertise in machine learning with offensive security skills to probe both the AI models themselves and their surrounding infrastructure, integration points, and data pipelines.

Whether you're deploying a customer-facing LLM chatbot, an internal AI coding assistant, or a complex multi-agent autonomous system, our assessment provides assurance that your AI behaves safely and securely under adversarial conditions — and delivers actionable guidance to fix what doesn't.

74% of AI deployments have no security testing before production Gartner AI Security Survey
#1 Prompt injection is the top LLM attack vector OWASP LLM Top 10
3× growth in AI-related security incidents in 2024 AI Incident Database
// PROCESS

Our Methodology

Structured adversarial testing aligned to OWASP LLM Top 10 and MITRE ATLAS — covering the full AI attack surface from prompt to infrastructure.

01
AI System Mapping
Architecture review and LLM-specific threat modeling.
02
Prompt Injection
Direct & indirect injection to override system prompts.
03
Jailbreaking
Guardrail bypass via roleplay, encoding, multi-turn manipulation.
04
Model & Data Pipeline
Extraction attacks, membership inference, poisoning.
05
Agent & Plugin
Tool injection, unauthorized plugin use, SSRF via LLM calls.
06
Infrastructure & API
LLM API auth, rate limiting, RAG pipeline, vector DB security.
// SCOPE

What We Test

The complete AI attack surface — from LLM-specific prompt attacks and model security to agentic systems and the infrastructure powering your AI deployment.

LLM-Specific Attacks

  • Direct and indirect prompt injection
  • System prompt exfiltration
  • Jailbreaking and guardrail bypass
  • Context window manipulation
  • Token smuggling and encoding attacks

Model Security

  • Model extraction and inversion attacks
  • Membership inference attacks
  • Adversarial examples and evasion
  • Training data poisoning (if in scope)
  • Embedding inversion via RAG queries

Agentic Systems

  • Tool call injection attacks
  • Unauthorized tool and plugin use
  • Multi-agent trust chain exploitation
  • SSRF via LLM-controlled HTTP calls
  • Insecure code execution in sandboxes

Infrastructure

  • LLM API security (auth, rate limits, IDOR)
  • RAG pipeline data leakage
  • Vector database access controls
  • MLOps pipeline security
  • Model serving infrastructure hardening
OWASP LLM Top 10
LLM01 Prompt Injection
LLM02 Insecure Output Handling
LLM03 Training Data Poisoning
LLM04 Model Denial of Service
LLM05 Supply Chain Vulnerabilities
LLM06 Sensitive Information Disclosure
LLM07 Insecure Plugin Design
LLM08 Excessive Agency
LLM09 Overreliance
LLM10 Model Theft
100% Coverage
// DELIVERABLES

What You Receive

Executive Summary

AI risk narrative for leadership: what the AI system can be manipulated to do, business impact scenarios, and strategic recommendations for safe AI deployment.

Technical Report

Detailed vulnerability report with working prompt injection payloads, jailbreak evidence, OWASP LLM Top 10 mapping, MITRE ATLAS technique IDs, and reproduction steps.

Remediation Guide

Developer-ready fix guidance: system prompt hardening, input validation, output filtering, guardrail improvements, and secure agentic architecture patterns.

Retest Verification

Complimentary retest of all identified findings after remediation, with updated OWASP LLM Top 10 compliance attestation and residual risk summary.

// TOOLS & STANDARDS

How We Work

Garak PyRIT (Microsoft) Burp Suite OWASP ZAP ART (IBM) Foolbox LangChain Red Custom Injection FW Adversarial Robustness MLflow Security Rebuff Custom Python
// FRAMEWORKS

Standards We Follow

OWASP LLM Top 10 (2025)
MITRE ATLAS (Adversarial Threat Landscape for AI)
NIST AI Risk Management Framework (AI RMF)
EU AI Act Security Considerations
Google Secure AI Framework (SAIF)
NIST SP 800-115 (Infrastructure layers)

Is Your AI System
Truly Secure?

Before a prompt injection attack manipulates your AI into a security incident, let our specialists test its resilience under real adversarial conditions.