SharpSec

AI/LLM Security Assessment

AI agents are scanning networks, processing sensitive data, and making autonomous decisions. Every new capability is a new attack surface. We test your AI and LLM implementations for the vulnerabilities that traditional security testing doesn't cover, from prompt injection to agent manipulation.

The Problem

Why This Matters

LLM-powered applications introduce entirely new vulnerability classes. Prompt injection can exfiltrate data, bypass controls, or manipulate outputs. AI agents with tool access can be tricked into executing unauthorized actions. RAG systems can leak proprietary data through carefully crafted queries. Traditional pentesting methodologies don't cover these risks.

Scope

What We Test

Prompt injection testing (direct, indirect, multi-step chains)
Data leakage through model outputs and RAG retrieval
Agent security: can your AI agent be tricked into unauthorized actions?
Guardrail bypass and content filter evasion
Authentication and authorization across AI API chains
API security for AI endpoints (rate limiting, input validation, output filtering)
Integration security: how your AI connects to databases, APIs, and external services
Model output manipulation and steering

Methodology

How We Work

We combine traditional application security testing with AI-specific attack techniques. Our methodology covers the OWASP Top 10 for LLM Applications plus deep experience with agent manipulation and multi-step prompt injection chains. We test against your specific implementation, not generic models.

Deliverables

What You Get

AI-specific vulnerability findings with exploitation scenarios
Risk assessment specific to your AI use case
Architecture recommendations for secure AI deployment
Guardrail and filter improvement guidance
Remediation retesting

Compliance & Framework Support

EU AI Act (risk assessment requirements)DORA (ICT risk management, applicable to AI as ICT assets)NIST AI RMFISO 27001SOC 2 (CC4.1)

Why Us

Why SharpSec

Offensive AI research

Our portspoof.io blog published research on AI agent deception, specifically how deception technology confounds AI-powered reconnaissance. We understand both sides of the AI security equation.

Engineering depth

We build security software. We understand how AI systems are built, integrated, and where the trust boundaries break.

Full-stack coverage

We test from model behavior to API security to agent tool chains. Prompt injection is the starting point, not the whole engagement.

Practical remediation

AI security is new territory for most teams. Our reports include specific, implementable fixes rather than a list of theoretical risks.

Frequently Asked Questions

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