AI Security Audits & LLM Vulnerability Assessments
When applications integrate large language models, retrieval pipelines (RAG), and autonomous agent tools, they introduce new attack vectors that traditional scanners cannot detect. Verisynt provides thorough, human-led penetration testing and risk audits for production AI systems.
What we evaluate during an AI security audit
We test every layer of the AI workflow — from user inputs and system prompt protections to backend vector stores, tool integrations, and data egress controls.
Prompt Injection & Jailbreaks
Manual adversarial red teaming testing direct prompt injection, indirect prompt injection via untrusted third-party documents, and delimiter escape techniques designed to override system constraints.
RAG & Vector Store Access Control
Testing embedding retrieval boundaries. We verify whether low-privilege users can query embeddings or semantic search pipelines to extract confidential documents belonging to other tenants or administrative tiers.
Agent Tool Execution & Function Calling
Validating the safety of tool hooks (database write access, API invocations, shell execution). We test whether malicious inputs can trick the model into executing unauthorized system operations or data exports.
Sensitive Data Egress & PII Leakage
Auditing prompt payloads, system telemetry, and vendor API traffic to ensure that customer PII, internal intellectual property, and credential secrets are not transmitted to third-party model providers unintentionally.
Shadow AI & Workforce Tool Usage
Evaluating organizational risk stemming from unvetted browser extensions, consumer AI subscriptions, and unapproved developer copilot tools interacting with corporate code repositories.
Insecure Output Handling
Assessing whether downstream parsers render model outputs without sanitization, leading to cross-site scripting (XSS), SQL injection, or server-side request forgery (SSRF) triggered through AI responses.
How an AI security audit engagement works
We combine structured adversarial testing with collaborative debriefs to ensure your developers understand exact attack chains and how to remediate them.
Architecture Discovery
We review your AI system design, model endpoints, RAG data ingestion pipelines, authentication boundaries, and vendor dependencies.
Adversarial Red Teaming
Our security engineers execute targeted manual prompt injection, context escape tests, and authorization bypass attempts against staging or test endpoints.
Developer Remediation Guide
You receive concrete fix instructions, input-guardrail architectures, sanitization code patterns, and policy controls prioritized by real business risk.
Verification Re-test
Once your team applies fixes, we re-test the identified vulnerabilities to verify that attack vectors are properly neutralized before release.
Audit Deliverables
- ✓ Executive Risk Summary: Clear summary of AI risk posture for leadership, board members, and compliance stakeholders.
- ✓ Technical Findings Report: Step-by-step reproduction steps, payloads, and severity classifications for every identified weakness.
- ✓ Guardrail Implementation Guide: Recommended input/output filter rules, context separation patterns, and token validation logic.
- ✓ Attestation of Assessment: Formal documentation of third-party security evaluation for enterprise buyer reviews.
Scope & Pricing Factors
Pricing and timeline depend directly on the technical depth of your AI architecture rather than arbitrary pricing tiers. Key factors include:
- • Number of user-facing prompt endpoints and models
- • Presence of RAG vector databases and document ingestion pipelines
- • Scope of autonomous agent tool hooks and function-calling permissions
- • Regulatory requirements (HIPAA, GDPR, SOC 2 alignment)
Common questions about AI security assessments
How does an AI security audit differ from standard penetration testing?
Traditional penetration testing evaluates web frameworks, network ports, and API endpoints for standard software vulnerabilities like SQL injection or broken authentication. An AI security audit focuses on model-specific attack vectors: prompt manipulation, system instruction bypass, training data or context extraction, semantic retrieval flaws in vector databases, and uncontrolled tool execution by autonomous agents.
Do we need to share our proprietary model weights or source code?
No. We perform both black-box assessments (interacting with your application solely through API endpoints and user interfaces) and gray-box assessments (reviewing system prompts and architecture diagrams under strict non-disclosure agreements). You never need to share raw model weights.
How long does an AI security assessment typically take?
Most focused assessments of single GenAI features or LLM integrations take 1 to 2 weeks, while full enterprise audits encompassing multi-tenant RAG systems and autonomous agent ecosystems take 2 to 3 weeks including debrief and remediation verification.
Ready to audit your AI attack surface?
Talk with a security specialist to define scope, timeline, and testing parameters.