AI Security Testing: LLM and RAG Penetration Testing, AI Agent Red Teaming, Bias Testing, and AI System Registers
AI security testing for chatbots, copilots, RAG applications, and AI agents. We attack your AI the way a real adversary would, against the OWASP Top 10 for LLM Applications and the OWASP Top 10 for Agentic Applications, test models for bias and robustness, and help you keep an AI system register that stands up to ISO 42001, NIST AI RMF, and the EU AI Act. Every finding is validated, ranked, and retested.
What Is AI Security Testing?
AI security testing checks whether an AI system can be tricked, misused, or made to leak data. Testers attack the model, the prompts, the data it retrieves, and the tools it can call, then show what an attacker could achieve and how to stop it. It covers large language model (LLM) apps, retrieval-augmented generation (RAG), and AI agents that take actions on their own.
AI breaks the assumptions of normal penetration testing. Attacks arrive as plain language, outputs change from one run to the next, and an agent with access to email, code, or payments can do real damage when it is fooled. A web app test alone will not find these problems, which is why AI needs its own test plan alongside our penetration testing.
Testing also produces the evidence governance frameworks ask for. Results feed straight into ISO 42001, the NIST AI RMF, and EU AI Act duties on accuracy, robustness, and cybersecurity, and into our AI governance work.
AI Security in 2026: Agentic Risks Named, EU Deadlines Moved, Testing Expected
Agents now have their own risk list. OWASP published the Top 10 for Agentic Applications in December 2025, alongside its 2025 Top 10 for LLM Applications. Goal hijacking, tool misuse, and memory poisoning are now standard test cases.
The EU AI Act timeline has shifted, not disappeared. The Digital Omnibus on AI, in force since 27 July 2026, moved duties for stand-alone high-risk AI systems to 2 December 2027 and for AI in regulated products to 2 August 2028. Transparency duties, such as telling people they are talking to AI, have applied since 2 August 2026, and general-purpose AI rules were not delayed. High-risk systems must still show accuracy, robustness, and cybersecurity.
US rules focus on bias and notice. New York City requires yearly independent bias audits of automated hiring tools. Colorado replaced its 2024 AI Act with SB 26-189, which applies from 1 January 2027 and centres on documentation, notice, and human review.
Sources: OWASP Agentic Top 10 summary · EU Digital Omnibus on AI (NLR) · Colorado SB 26-189 (Eckert Seamans)
Types of AI Security Testing
Each AI system gets a test plan built from how it is used, what data it sees, and what it is allowed to do.
| Test type | What it covers | Typical findings |
|---|---|---|
| LLM application penetration testing | Chatbots, copilots, and assistants, plus the web app and APIs around them | Prompt injection, system prompt leaks, unsafe output rendering |
| RAG testing | Retrieval, vector stores, document permissions, and source handling | Users retrieving other users' documents, poisoned content steering answers |
| AI agent and tool testing | Agents, plugins, and MCP or API tools, with their permissions and memory | Goal hijacking, tool misuse, privilege abuse, code execution |
| AI red teaming | Realistic misuse scenarios and jailbreak campaigns, using MITRE ATLAS techniques | Harmful or off-policy outputs, safety filter bypasses |
| Model and AI supply chain review | Third-party models, datasets, libraries, and plugins | Unvetted models, outdated components, weak licensing records |
| Bias and robustness testing | Fairness across groups, accuracy, drift, and resistance to adversarial input | Unequal error rates, unstable outputs, easy evasion |
| AI system register | An inventory of AI systems with owners, purpose, data, and risk tier | Shadow AI, unclassified high-risk uses, missing owners |
OWASP LLM Top 10 (2025) Testing: What We Check for Each Risk
The most widely used list of LLM application risks, and how each one shows up in our test plan.
| Risk | What it means | What we test |
|---|---|---|
| LLM01 Prompt Injection | Inputs that override the model's instructions | Direct and indirect injection through chats, files, web pages, and emails |
| LLM02 Sensitive Information Disclosure | The model reveals personal, secret, or business data | Extraction of other users' data, keys, and internal content |
| LLM03 Supply Chain | Risky models, datasets, or components | Model sources, versions, and third-party plugins |
| LLM04 Data and Model Poisoning | Tampered training, fine-tuning, or retrieval data | Who can write to data sources, and the effect of poisoned content |
| LLM05 Improper Output Handling | Model output trusted by other systems | Script injection, SQL, and commands passed through model output |
| LLM06 Excessive Agency | Too much power, access, or autonomy | Tool permissions, approval steps, and what a hijacked model can do |
| LLM07 System Prompt Leakage | Hidden instructions exposed | Extraction attempts, and secrets wrongly placed in prompts |
| LLM08 Vector and Embedding Weaknesses | Flaws in RAG storage and retrieval | Cross-tenant retrieval, embedding leakage, and access controls |
| LLM09 Misinformation | Confident but false output that people rely on | Hallucination rates on your own use cases, and grounding checks |
| LLM10 Unbounded Consumption | Abuse that drives cost or denies service | Rate limits, token caps, and denial-of-wallet scenarios |
Agentic AI Security Testing: The OWASP Agentic Top 10 (2026)
AI agents plan, remember, and act. These are the risks that come with that power, and how we test them.
| Risk | What we test |
|---|---|
| ASI01 Agent Goal Hijack | Whether planted content can redirect the agent's objective |
| ASI02 Tool Misuse and Exploitation | Whether legitimate tools can be turned against you |
| ASI03 Identity and Privilege Abuse | Which credentials the agent holds, and whether they can be borrowed or escalated |
| ASI04 Agentic Supply Chain Vulnerabilities | Third-party agents, tools, MCP servers, and prompts you depend on |
| ASI05 Unexpected Code Execution | Whether the agent can be made to write and run attacker code |
| ASI06 Memory and Context Poisoning | Whether false information can be planted in memory and reused later |
| ASI07 Insecure Inter-Agent Communication | Spoofing and tampering between cooperating agents |
| ASI08 Cascading Failures | How one bad step spreads across agents and systems |
| ASI09 Human-Agent Trust Exploitation | Whether the agent can mislead users into approving harmful actions |
| ASI10 Rogue Agents | Monitoring and kill switches for agents acting outside their purpose |
RAG Security Testing: Where Retrieval Goes Wrong
Retrieval-augmented generation connects an LLM to your documents. That connection is where most real-world data leaks happen.
What goes wrong
- Users retrieve documents they are not allowed to see
- One customer's data appears in another customer's answers
- Poisoned or hidden text in a document steers the model
- Sensitive data is copied into embeddings and logs
- Answers cite sources that do not support them
How we test it
- Retrieval attempts from each user role and tenant
- Documents seeded with indirect prompt injection
- Vector store access, isolation, and deletion checks
- Grounding and citation checks on your real questions
- Logging and retention review for prompts and outputs
AI System Registers and Bias and Robustness Testing
Security testing tells you whether AI can be attacked. These two services show regulators and customers that you know what AI you run and that it behaves fairly.
AI system register
A living inventory of every AI system you build or use: its owner, purpose, model or vendor, data, users, risk tier, human oversight, and testing status. It is the starting point for ISO 42001, the NIST AI RMF "Map" function, and EU AI Act risk classification, and it surfaces shadow AI.
If we certify, or are due to certify, your ISO 42001 management system, we review your register but do not build it for you. That keeps our certification impartial.
Bias and robustness testing
- Outcomes and error rates compared across groups, including selection-rate impact ratios
- Accuracy on your own data and use cases
- Robustness to typos, rephrasing, and adversarial inputs
- Drift checks over time and after model changes
- Evidence for NYC bias audits, EU AI Act data and accuracy duties, and customer reviews
How Our AI Security Testing Works
Six steps, from a threat model to a retested report.
Launching an AI feature, or facing a customer AI questionnaire? A free scoping review tells you which tests matter first.
Book a free scoping reviewHow AI Testing Supports ISO 42001, NIST AI RMF, and the EU AI Act
| Framework | What it expects | How testing helps |
|---|---|---|
| ISO/IEC 42001 | AI risk and impact assessment, controls, and monitoring | Test results show controls work, and feed risk treatment |
| NIST AI RMF and AI 600-1 | Map, measure, and manage AI risks, including generative AI | Red teaming and robustness tests are "Measure" evidence |
| EU AI Act | Accuracy, robustness, and cybersecurity for high-risk AI; adversarial testing for systemic-risk models | Documented testing for technical files and conformity work |
| OWASP and MITRE ATLAS | Common language for AI threats | Every finding mapped to a recognized risk or technique |
| ISO 27001 and SOC 2 | Security testing of in-scope systems | AI features covered in the same evidence set |
| CCPA rules on automated decisions | Risk assessments for significant automated decisions | Bias and accuracy results inform the assessment |
AI Security Testing Cost and Timeline
There is no list price. Effort depends on how many AI features, agents, and data sources are in scope, and a free scoping review gives you a tailored proposal.
What sets the effort
- AI features — chatbots, copilots, and agents in scope
- Tools and permissions — what agents can read, write, and run
- Data sources — RAG collections, tenants, and roles
- Depth — OWASP-based test, full red team, or both
- Extras — bias testing, AI register, and retesting rounds
Planning the timeline
| Scoping and authorization | Agreed before testing starts |
|---|---|
| Testing window | Set in your proposal |
| Retest | After your team's fixes |
| Repeat | After model, prompt, or tool changes, and at least yearly |
How to Choose an AI Security Testing Provider
Questions worth asking, and how Accredify Global answers them.
| Question to ask | Accredify Global's answer |
|---|---|
| Do you test agents and tools, not just the chat box? | Yes. Tool permissions and agent actions are usually where the real risk is. |
| Is it more than running a jailbreak list? | Yes. Automated payloads find weak spots; testers chain them into real attacks. |
| Can findings support ISO 42001 or the EU AI Act? | Yes. Each finding is mapped to the frameworks you report against. |
| If you certify our ISO 42001, can you also test? | Testing, yes, by people separate from your certification audit. Building your AI register, no. |
| Do you fix the findings? | No. Your team fixes them; we retest. |
| How is it priced? | A tailored proposal after a free scoping review. |
What You Receive From AI Security Testing
How your AI could be attacked, in plain language.
Each with evidence, severity, and the OWASP or ATLAS reference.
Guardrails, permissions, and design changes your engineers can apply.
Where in scope: metrics by group, with methods explained.
Where in scope: built or reviewed, with risk tiers.
Fixes confirmed, and a letter to share with customers.
Frequently Asked Questions About AI Security Testing
Answers on LLM and agent testing, RAG, OWASP, red teaming, bias, registers, and cost.
What is AI security testing?
AI security testing checks whether an AI system can be tricked, misused, or made to leak data. Testers attack the model, prompts, retrieved data, and the tools the AI can call, then report what an attacker could achieve and how to fix it. It covers LLM apps, RAG applications, and AI agents.
What is LLM penetration testing?
LLM penetration testing is a security test of an application built on a large language model, such as a chatbot or copilot, together with the web app and APIs around it. It focuses on risks like prompt injection, sensitive data disclosure, unsafe output handling, and excessive agency, using the OWASP Top 10 for LLM Applications as a baseline.
What is the OWASP Top 10 for LLM Applications?
It is OWASP's list of the most critical risks for LLM applications. The 2025 edition covers prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption.
What is the OWASP Top 10 for Agentic Applications?
Published in December 2025, it lists the main risks for AI agents that plan and act: agent goal hijack, tool misuse, identity and privilege abuse, agentic supply chain vulnerabilities, unexpected code execution, memory and context poisoning, insecure inter-agent communication, cascading failures, human-agent trust exploitation, and rogue agents.
How do you test a RAG application?
We test retrieval from every user role and tenant, seed documents with hidden instructions to check for indirect prompt injection, review vector store access and isolation, and check whether answers are grounded in the sources they cite. We also review how prompts, outputs, and embeddings are logged and retained.
What is AI red teaming?
AI red teaming is an adversarial exercise where testers act like real attackers or misusers, combining jailbreaks, prompt injection, and misuse scenarios to make an AI system behave in harmful or off-policy ways. Findings are mapped to MITRE ATLAS techniques and OWASP risks.
How is AI security testing different from a normal penetration test?
A normal penetration test looks for code and configuration flaws. AI attacks arrive as natural language, outputs vary between runs, and agents can take real actions with their tools. AI testing adds prompt-based attacks, retrieval and memory abuse, and permission checks on agent tools, alongside the usual web and API testing.
What is an AI system register?
An AI system register is an inventory of the AI systems an organization builds or uses, with each system's owner, purpose, model or vendor, data, users, risk tier, human oversight, and testing status. It underpins ISO 42001, the NIST AI RMF, and EU AI Act risk classification.
Can you build our AI system register if you certify our ISO 42001?
No. Building the register is part of implementing an AI management system, so a certification body cannot do it for a client it certifies. If we certify, or are due to certify, your ISO 42001 system, we can review your register instead. Other clients can ask us to build one.
What is bias testing for AI?
Bias testing compares an AI system's outcomes and error rates across groups, such as selection rates by sex or race and ethnicity, to find unfair differences. It is required for automated hiring tools in New York City through yearly independent bias audits, and supports EU AI Act and customer requirements.
What is robustness testing for AI?
Robustness testing checks whether an AI system keeps giving correct, stable answers when inputs change, for example with typos, rephrasing, unusual data, or deliberate adversarial inputs. It also checks for drift after model or data changes.
Does the EU AI Act require AI security testing?
For high-risk AI systems, the EU AI Act requires appropriate accuracy, robustness, and cybersecurity, which testing helps demonstrate. Providers of general-purpose AI models with systemic risk must also carry out adversarial testing. After the Digital Omnibus, most stand-alone high-risk duties apply from 2 December 2027.
How often should AI systems be tested?
Test before launch, after significant changes to the model, prompts, retrieval data, or agent tools, and at least once a year. Fast-changing AI products often add lighter checks with each release.
Do you fix the findings?
No. Your team fixes them using the guidance in our report, such as tighter tool permissions, input and output filters, and retrieval access controls. We then retest and update the report.
How much does AI security testing cost?
There is no list price. Effort depends on the number of AI features and agents, the tools and data they can reach, and the depth of testing. A free scoping review gives you a tailored proposal.
Find Out How Your AI Can Be Fooled Before Someone Else Does
Tell us about your AI features, agents, and data. We'll recommend the right mix of LLM testing, red teaming, bias testing, and register work, and send a tailored proposal.
Book your free scoping review →Last reviewed by the Accredify Global security team.