Security Services

AI & LLM Security Assessment

Assess AI-enabled applications and LLM integrations for prompt injection, data leakage, trust-boundary violations, and abuse paths that traditional application testing doesn't cover.

Overview

AI-enabled applications and LLM integrations create security exposure patterns that traditional penetration testing methodologies don't address. Prompt injection, indirect prompt injection through retrieved content, excessive agency in tool-calling workflows, data leakage through model responses, and trust-boundary violations between user input and system actions all require specialized assessment.

An AI security assessment validates how your LLM-powered features handle adversarial input, whether tool-calling and retrieval workflows maintain appropriate trust boundaries, and whether the system can be manipulated into disclosing sensitive data, taking unauthorized actions, or bypassing human review steps. The assessment follows OWASP's AI/LLM security guidance adapted to your specific implementation.

What This Covers

Prompt injection and indirect prompt injection testing across input surfaces

Tool-calling, function execution, and agent-action boundary validation

Data leakage assessment for sensitive information in model responses

RAG pipeline and retrieval-augmented workflow trust-boundary testing

System prompt extraction and instruction-override resistance validation

Findings with reproduction steps and remediation guidance specific to LLM security

Operational Outcomes

What becomes safer once your AI implementation has been adversarially tested.

Prompt injection, data leakage, and trust-boundary violations are identified before they reach production users.
Tool-calling, agent actions, and retrieval workflows are validated for appropriate guardrails and human review steps.
Your team has a clear picture of where AI-specific controls need hardening — distinct from traditional application security findings.

You're deploying AI-powered features that interact with sensitive data, execute actions, or face untrusted user input — and you need validation that goes beyond traditional application testing.

Engagement Flow

Scope, validate, and follow through.

Security work should prove something useful, document it clearly, and make the next move easier to execute.

Scope & authorize

Clarify environment, boundaries, timing, and who sees results.

Test & document

Evidence gathered deliberately, findings written for operators and leadership.

Remediate & retest

Fix guidance, retest support, and recurring ownership when needed.

Remediation can cycle back to scope for periodic reassessment

Pressure Profile

Pressure patterns that usually point here.

You're deploying AI-powered features that interact with sensitive data, execute actions, or face untrusted user input — and you need validation that goes beyond traditional application testing.

Scoping Conversation

Define the right depth, timing, and follow-through.

Discovery should clarify scope, environment, timing, reporting needs, and whether the next move is testing, recurring leadership, or a compliance engagement.

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