AI Security Testing
Test Your AI Applications Before Someone Else Does
AI security testing is an authorized assessment of applications built on large language models, such as chatbots, copilots, retrieval-augmented generation (RAG) systems and AI agents with tool access, to find ways they can be manipulated into leaking data or taking unsafe actions. It is often called LLM penetration testing or AI red teaming. It is for organizations that have connected AI to customers, internal documents or business systems and need to know where it can be misused.
Redbeard Intelligence and Investigations is based in Florida and tests AI systems for organizations nationwide, usually remotely. Our team already investigates AI-enabled fraud and deepfake schemes, so we see how criminals misuse these tools against real people. That perspective shapes the scenarios we test.
Why AI Security Testing Matters
AI applications follow instructions written in plain language, so anyone who can put text in front of them may be able to change what they do.
- Prompt Injection Exposure
Tests whether direct or hidden instructions can override your system prompt and guardrails.
- Sensitive Data Leakage
Checks whether the AI reveals personal data, secrets, system prompts or other customers’ content.
- Safer AI Agents
Examines which tools and permissions an agent holds and whether risky actions need human approval.
- Permission-Aware Retrieval
Confirms that RAG search respects the same access rights users have to the source documents.
- Downstream Output Risks
Looks for model output that browsers, scripts or other systems render or execute unsafely.
- Real-World Abuse Insight
Shaped by our investigations into AI-enabled fraud and deepfake schemes.
What Is in Scope
Testing covers the AI features and the application, data and infrastructure around them, including:
- Customer-facing chatbots, support assistants and AI search features.
- Internal copilots and enterprise assistants connected to email, file shares or knowledge bases.
- RAG pipelines, including document ingestion, vector stores and permission checks at retrieval time.
- AI agents that call tools, run code, send messages, edit files or keep memory between sessions.
- The surrounding application: login and authentication, tenant isolation, rate limits, APIs and logging.
- Supply-chain exposure from model providers, plug-ins, model files, datasets and machine learning libraries.
What We Test For
- Direct prompt injection and jailbreaks that talk the model out of its instructions or safety controls.
- Indirect prompt injection, where hidden instructions arrive inside a web page, email, document or ticket the AI reads.
- Sensitive information disclosure, including leaked system prompts, credentials, personal data and other tenants’ content.
- Insecure output handling, where generated text is passed to a browser, database or shell without proper encoding or checks.
- Excessive agency, where an agent holds broader tools or permissions than its task needs, or acts without human confirmation.
- Retrieval data exposure from over-shared, poisoned or poorly filtered knowledge sources.
- Access-control and abuse risks, such as weak authentication, missing rate limits, runaway usage costs and gaps in content safety controls.
Findings can be mapped to public references such as the OWASP Top 10 for LLM Applications and MITRE ATLAS, and governance observations can reference the NIST AI Risk Management Framework. These are reference points, not certifications.
How an Engagement Runs
- Scoping. We agree on applications, environments, test accounts, data sensitivity and rules of engagement.
- Architecture review. Testers map models, prompts, data sources, tools and trust boundaries into a threat model.
- Automated probing. Adversarial prompt sets exercise common injection, leakage and jailbreak patterns at volume.
- Manual attack chains. Testers build multi-step scenarios, such as a planted document that steers an agent toward an unsafe tool call.
- Application and API testing. Conventional web, API and cloud checks cover the surrounding code and infrastructure.
- Repeated trials. Because AI output varies, promising attacks are rerun and their success rates noted.
- Reporting and retest. Findings arrive with sanitized reproduction steps and are retested after fixes or model updates.
Authorization and Rules of Engagement
Testing begins only after written authorization from someone with legal authority over the AI application and the systems it connects to. Where a third-party model provider, cloud platform or hosting company is involved, we confirm the testing is permitted under its terms and obtain any required approvals. Each engagement has a signed scope and rules of engagement, named emergency contacts, agreed testing windows, and a stop and deconfliction process so testing can pause at once if it affects production users, costs or a real incident. Testers use dedicated accounts and synthetic data where possible, and any real data encountered stays confidential.
What You Receive
- An executive summary written for leadership and risk committees.
- A threat model and data-flow diagram of your AI application.
- Risk-ranked findings mapped to the OWASP Top 10 for LLM Applications and MITRE ATLAS.
- Sanitized, reproducible test cases your developers can rerun.
- Practical fixes such as tighter tool permissions, human approval for risky actions, output encoding, retrieval that respects user permissions, and filtering of inputs and outputs.
- Retest results and a summary you can use when customers ask how your AI is tested.
What Affects Cost
Cost depends on the number of AI applications and environments, the tools and data connectors an agent can use, whether RAG pipelines and source code are in scope, how much conventional application testing is included, the user roles and tenants to compare, and whether governance review or retesting after model updates is needed.
Who It Is For and When to Schedule It
AI security testing suits software companies shipping AI features, organizations connecting assistants to internal data, and legal, healthcare and financial teams using AI with confidential information. Common triggers include a new AI launch, a customer questionnaire about AI controls, a governance committee review, a model or provider change, or harmful AI output. Pair this work with penetration testing for the wider platform, or with red team and attack simulation to see AI misuse within a full attack scenario. If an AI tool may already have exposed data, our incident response team can help.
Frequently Asked Questions
What is LLM penetration testing?
LLM penetration testing is an authorized attempt to make an application built on a large language model misbehave the way an attacker would. Testers try prompt injection, jailbreaks, data extraction and unsafe tool use, then test the surrounding application, APIs and permissions. The goal is to find and fix weaknesses before real users or attackers discover them.
What is prompt injection, and how is it tested?
Prompt injection is when text supplied to an AI system overrides its intended instructions. Direct injection comes from a user typing into the chat, while indirect injection hides instructions inside content the AI reads, such as a web page, email or document. We test both with automated prompt sets and hand-built, multi-step scenarios, repeating promising attempts because AI output varies.
Can our AI chatbot leak customer data?
It can, if the chatbot or the systems behind it can reach data the current user should not see. Leaks often come from over-broad retrieval, shared memory between sessions, system prompts containing secrets, or connectors with excessive permissions. Testing checks whether one user can coax out another user’s records, internal documents or credentials, and recommends controls to close those paths.
Do we still need testing if our AI uses a third-party hosted model?
Yes, in most cases. The model provider secures its own platform, but your prompts, data connections, retrieval permissions, tools and output handling are your responsibility, and many AI application weaknesses sit in that integration layer. Testing focuses on how your application uses the model, within the provider’s usage terms, rather than attacking the provider’s infrastructure.
How is AI security testing different from a web application pen test?
AI security testing covers risks that traditional web testing does not, such as prompt injection, model output steering an agent’s actions, and retrieval that ignores document permissions. Because the same prompt can produce different answers, testers repeat attacks and record success rates. A thorough engagement still includes conventional application, API and cloud testing, because the model is only one part of the system.
Explore Redbeard Security Services
- Security Assessment and Testing Services
- Physical Security Assessments
- Physical Penetration Testing
- Social Engineering Testing
- Vulnerability Assessment Services
- Penetration Testing Services
- Mobile Application Penetration Testing
- Red Team and Attack Simulation
- Cyber Threat and Incident Response
- Cybersecurity Advisory
- Contact Redbeard Intelligence and Investigations
Related services: cyber investigations and digital forensics.
Find the Weak Points in Your AI Before Someone Else Does
AI applications can be steered in ways traditional testing does not catch. Redbeard Intelligence and Investigations can help you test chatbots, RAG systems and AI agents against realistic misuse and turn the results into practical fixes. Call (888) 564-8300 or email contact@redbeardpi.com to discuss your AI systems in confidence.
What is the cost? | how does it work?
At Redbeard Intelligence & Investigations, we offer upfront, transparent pricing that reflects the specific details and complexity of each case. Instead of vague estimates or hidden charges, we provide clear, upfront pricing tailored to your investigation’s unique requirements—without unexpected fees, hidden costs, or additional unexpected expenses. Our goal is to offer efficient, high-quality investigative and intelligence solutions with transparency and peace of mind.
To begin an investigation, intelligence operation, or technical security assessment, the first step is a confidential consultation via phone or email with one of our experienced professionals. During this initial discussion, we will gather key details about your situation to determine the most effective strategy tailored to your needs. Whether you require private investigative services, intelligence gathering, or technical security solutions, our team will develop a customized plan designed to achieve your objectives. Based on the specific requirements of your case, we will then provide a transparent price quote and/or invoice for the cost of the services needed. Throughout the process, we ensure clear and consistent communication, keeping you informed of any significant developments. Upon completion, you will receive a comprehensive written report detailing our findings, along with any supporting evidence such as photos, video documentation, or technical security assessments.
Contact us through phone or email for an initial discussion and provide key details about your needs.
Based on the specific requirements of your case, you'll will receive a flat-rate price quote and invoice for the cost of the services needed.
With pricing and mutual terms accepted, payment will be collected for the predetermined costs, and you'll officially become our client.
Throughout the process, we keep you informed of any significant developments. Upon completion, you will receive a written report detailing our findings, along with any supporting evidence or documentation.
