AI Red Teaming & LLM Penetration Testing Services

AI red teaming, also called AI hacking or AI penetration testing, is the practice of attacking artificial intelligence systems the way a real adversary would, to find exploitable weaknesses in models, training data, retrieval pipelines, AI agents, and the infrastructure around them before attackers do.

Traditional penetration testing targets applications, networks, and infrastructure. AI red teaming targets the model and everything it can see and do: the prompts it accepts, the documents it retrieves, the tools it can call, and the data it was trained on. That includes forcing misclassification with adversarial inputs, extracting training records through membership inference and model inversion, poisoning training or retrieval data, bypassing safety controls with prompt injection and jailbreaks, and abusing the permissions of AI agents and Model Context Protocol (MCP) servers.

These attacks need expertise conventional testing doesn’t cover: how models are built and trained, how retrieval and tool use change the attack surface, and how AI systems behave under adversarial pressure. VerSprite’s AI red teaming services are built to close that gap.

Frameworks We Map To

Every VerSprite AI security assessment maps findings to the frameworks your security, risk, and compliance teams already use:

• OWASP Top 10 for LLM Applications (2025) — including prompt injection (LLM01), sensitive information disclosure (LLM02), supply chain (LLM03), data and model poisoning (LLM04), improper output handling (LLM05), excessive agency (LLM06), system prompt leakage (LLM07), and vector and embedding weaknesses (LLM08)
• MITRE ATLAS — adversary tactics and techniques against AI systems, used to structure attack scenarios and report findings in a common language
• NIST AI Risk Management Framework (AI RMF) and its Generative AI Profile — findings and remediation framed for AI governance programs
• ISO/IEC 42001 and the EU AI Act — testing evidence that supports AI management system and robustness requirements for regulated or high-risk AI

VerSprite assesses AI systems using PASTA (Process for Attack Simulation and Threat Analysis), the risk-centric, seven-stage threat modeling methodology co-created by VerSprite CEO Tony UcedaVélez and documented in Risk Centric Threat Modeling (Wiley, 2015). Applying PASTA to AI ties every test to a business objective and a credible threat, rather than running a generic checklist of prompts.

VerSprite scopes each engagement to your systems rather than running every technique below by default.

LLM & Generative AI Applications

• Prompt injection — direct and indirect injection that bypasses safety filters, overrides system instructions, or extracts restricted information
• Jailbreaking — single- and multi-turn attacks against alignment and safety mechanisms
• System prompt leakage — exposure of instructions, credentials, or business logic embedded in prompts
• Output handling — model output that reaches browsers, databases, or shells unsanitized, enabling XSS, injection, or code execution

AI Agents & Model Context Protocol (MCP)

• Excessive agency — agents that can take actions beyond what the user is authorized to do
• Tool abuse — manipulating an agent into calling tools with attacker-chosen parameters or chaining tools into unintended workflows
• MCP server security — trust boundaries between agents and MCP servers, over-permissioned tokens, tool-description poisoning, and confused-deputy attacks
• Multi-agent trust — instructions or data passed between agents without validation

RAG Pipelines & Vector Stores

• RAG poisoning — planting malicious content in retrieval sources to steer model behavior
• Retrieval access control — whether users can retrieve documents they shouldn’t see, including cross-tenant leakage in shared vector stores
• Embedding weaknesses — inversion and manipulation of stored embeddings

Adversarial Machine Learning

• Evasion attacks — inputs that cause misclassification while appearing normal
• Data poisoning — whether training data can be manipulated to degrade performance or plant backdoors
• Model extraction — whether a proprietary model can be reconstructed through strategic querying
• Membership inference and model inversion — whether specific records can be identified or reconstructed from training data

AI Infrastructure & MLOps

• Pipeline security — CI/CD for model training and deployment
• Model registry and supply chain — access controls, artifact integrity, and third-party model and dataset provenance
• Data pipeline security — ingestion, preprocessing, and feature-engineering systems
• Container and orchestration — ML workloads and their runtime environments

What You Receive from an AI Red Teaming Engagement

An effective AI red teaming assessment should provide more than a list of failed prompts. Enterprise security teams need evidence of how vulnerabilities affect the AI system, which trust boundaries were crossed, and which controls must change to reduce business risk.

VerSprite’s AI security assessment deliverables can be structured around documented attack scenarios, validated security findings, affected system components, business-impact analysis, and remediation recommendations.

Depending on the authorized assessment scope, deliverables may include:

• Attack scenario documentation: Tested adversarial techniques, attack paths, and observed AI system behavior.
• Validated findings: Evidence demonstrating whether vulnerabilities could be exploited and under what conditions.
• Framework mapping: Relevant mappings to OWASP LLM Top 10, MITRE ATLAS, and applicable AI risk management guidance.
• Business risk prioritization: Findings evaluated against the potential consequences for sensitive data, operations, and business objectives.
• Remediation guidance: Technical recommendations for strengthening model controls, retrieval security, agent permissions, and infrastructure.
• Executive and technical reporting: Clear communication of risk for security leadership and actionable findings for engineering teams.

Assessment deliverables should be agreed upon during scoping so security leaders and technical teams receive the evidence needed to make informed remediation decisions.

VerSprite offers both. This page covers human-led AI red teaming engagements: scoped, adversarial testing by offensive security practitioners, built around your architecture and business risk. For continuous, automated testing, Knife, VerSprite’s automated AI pen testing platform, is included in our Penetration Testing as a Service (PTaaS) offering. Many clients use a human-led engagement to establish depth, then PTaaS to keep coverage current as models and prompts change.

VerSprite delivers AI red teaming across industries where AI drives critical decisions or touches sensitive data.

Financial Services & FinTech — Adversarially test fraud-detection, credit-scoring, and trading models; assess LLM-powered financial assistants and agents for prompt injection, data leakage, and unauthorized transactions.

Healthcare & Life Sciences — Test clinical and diagnostic AI for data poisoning, model inversion, and unauthorized inference against systems processing ePHI.

SaaS & Technology Providers — Red team AI copilots, customer-facing AI features, autonomous agents, and MCP integrations across cloud-native, multi-tenant architectures.

Retail & E-Commerce — Test recommendation engines, pricing algorithms, and shopping assistants for manipulation, and AI-driven fraud against payment flows.

Manufacturing & Critical Infrastructure — Assess operational and predictive-maintenance AI in IT/OT environments for adversarial manipulation that could disrupt physical operations.

We Created the Methodology

PASTA, the risk-centric threat modeling framework VerSprite applies to AI, was co-created by our CEO and is used globally — including by organizations like GitLab. Our AI testing isn’t bolted onto a generic pentest; it’s grounded in a methodology we authored.

Risk-Based, Not Checklist-Based

We tie every finding to exploitability and business impact, so you get a prioritized view of real risk — not a catalog of theoretical prompt failures.

Human-Led Offensive Expertise

Our assessments are led by offensive security practitioners with adversarial experience, not automated scanning alone.

Proven Across Industries

Our team has assessed AI and ML systems across financial services, healthcare, technology, and other regulated sectors.