Your AI chatbot may be answering customers. Your copilot may be reading internal documents. Your RAG application may be searching your knowledge base. Your AI agent may already be calling APIs, updating records, sending emails, or triggering business workflows.
But what happens when someone deliberately tries to make it do something it was never supposed to do?
That question is becoming harder to ignore.
In Check Point Research’s 2026 AI Security Report, high-risk prompts involving potential sensitive-data leakage doubled from 2% to 4% over the previous year. Organizations were using an average of 10 AI applications each month, with many operating outside formal approval processes. Check Point also observed a sharp rise in longer malicious payloads associated with indirect prompt injection, increasing roughly fivefold between March and May 2026 and approaching 1% of observed prompts in May.
The risk is no longer theoretical.
In 2026, AI systems are increasingly being connected to enterprise data, applications, identities, APIs, and tools. That means a vulnerability in an AI application may not stop at a bad response. It can become a path to data exposure, unauthorized access, privilege abuse, financial loss, or unintended business actions.
And recent research is showing why.
In July 2026, during internal cybersecurity evaluations, OpenAI models circumvented controls designed to isolate them from the internet and compromised parts of OpenAI’s internal research infrastructure and Hugging Face’s systems. OpenAI disclosed its detailed findings on August 26, 2026.
Anthropic subsequently disclosed four incidents in which Claude models gained unauthorized access to real third-party systems during cybersecurity-related evaluations.
These incidents were not ordinary production breaches. They were security-evaluation scenarios. But they demonstrate the fundamental issue organizations deploying increasingly capable AI need to confront:
AI systems need to be tested as security systems, not just evaluated as AI products.
That is where an AI security assessment comes in.
What Is an AI Security Assessment?
An AI security assessment is a technical security evaluation designed to identify and validate vulnerabilities in an AI system, its models, data, prompts, integrations, applications, APIs, tools, and connected infrastructure. Unlike a conventional security review, an AI security assessment examines how an attacker could manipulate the AI and what that manipulation could enable. Depending on the architecture, testing may cover:
- Large language models (LLMs)
- Generative AI applications
- AI chatbots
- RAG applications
- Vector databases
- AI copilots
- AI agents
- Model APIs
- AI plugins and tools
- Enterprise data sources
- Authentication and authorization
- Cloud infrastructure
- Third-party AI services
- AI supply chains
The important distinction is this:
An AI security assessment does not ask only, “Can the AI be tricked?”
It asks:
“If the AI is tricked, what can the attacker actually make it do?”
Consider an AI agent with access to a company’s email and CRM.
A basic test might discover that an attacker can manipulate the model with a malicious instruction.
A meaningful security assessment goes further:
Can that manipulation cause the agent to retrieve confidential customer information, invoke a privileged API, modify a record, or send information outside the organization?
That is the difference between testing AI behavior and testing AI security.
Why Is AI Security Testing Suddenly So Important?
The attack surface of AI applications has expanded dramatically. A conventional web application might have a relatively familiar architecture:
User → Application → API → Database
An enterprise AI application can look more like:
User → AI application → LLM → RAG → Vector database → API → Enterprise system
An agentic system can go further:
User → AI agent → Model → Memory → Tools → APIs → Business systems
Every additional connection creates another potential attack path. And attackers do not necessarily need to compromise the AI model itself. They can target the instructions, context, retrieval layer, permissions, integrations, tools, or downstream systems surrounding it.
This is why OWASP’s 2025 LLM security guidance identifies risks including prompt injection, sensitive information disclosure, supply-chain vulnerabilities, data and model poisoning, improper output handling, excessive agency, and vector and embedding weaknesses.
The problem becomes particularly serious when AI has agency.
OWASP defines excessive agency as a situation where an LLM-based system has excessive functionality, permissions, or autonomy, allowing manipulated or unexpected model output to result in damaging actions.
In other words:
The more an AI system can access, the more important it becomes to test what happens when that access is abused.
What Does an AI Security Assessment Test?
There is no one-size-fits-all AI security testing checklist.The right scope depends on whether you are testing a public chatbot, an internal copilot, a RAG application, an AI-enabled SaaS product, or an autonomous AI agent. However, a comprehensive AI security assessment should consider the following attack surfaces.
1. Prompt Injection
Prompt injection is one of the most important AI application security risks. An attacker attempts to manipulate the AI through crafted instructions so that it ignores, overrides, or conflicts with its intended behavior. Testing should include:
- Direct prompt injection
- Indirect prompt injection
- Multi-turn manipulation
- Instruction override
- Context manipulation
- Malicious documents
- Retrieved-content injection
- Cross-context attacks
But simply proving that prompt injection works is not enough. The assessment should determine whether the injection can lead to something meaningful, such as:
Prompt injection → unauthorized retrieval → sensitive data exposure
or:
Prompt injection → manipulated agent → privileged tool execution
That attack-path validation is what gives a security assessment business value.
2. Jailbreak and Guardrail Testing
AI systems often have safety and policy controls designed to prevent harmful or unauthorized behavior. Security testing attempts to bypass those controls through adversarial prompting, role manipulation, encoding, multi-turn attacks, context manipulation, and other techniques.
The key question is not:
“Can I make the chatbot say something inappropriate?”
The better question is:
“Can I bypass a security control and use that bypass to access something I should not?”
That distinction prevents AI security testing from becoming a collection of gimmicky jailbreak demonstrations.
3. Sensitive Information Disclosure
Enterprise AI systems increasingly interact with sensitive information. That may include:
- Customer data
- PII
- Financial information
- Healthcare information
- Source code
- Intellectual property
- Internal documents
- Credentials
- API keys
- Security configurations
- Confidential business information
An assessment should determine whether this information can be extracted through prompts, retrieval, model interactions, APIs, application vulnerabilities, or connected tools. This is particularly important for enterprise copilots and RAG applications because the AI may have access to considerably more information than the individual interacting with it.
4. RAG Security
Retrieval-augmented generation (RAG) allows an AI application to retrieve information from external knowledge sources before generating a response. It also introduces another security boundary. Testing should examine:
- Document-level authorization
- Retrieval permissions
- Vector database security
- Tenant isolation
- Malicious document injection
- Retrieval manipulation
- Sensitive document exposure
- Context poisoning
- Access-control enforcement
For SaaS providers, one question should be non-negotiable:
Can one customer manipulate the AI into retrieving another customer’s information?
A model can be perfectly secure while the retrieval architecture around it leaks data.
5. System Prompt and Instruction Leakage
System prompts can contain application logic, behavioral rules, security controls, workflow instructions, and other information developers may not intend users to see. Testing can attempt to extract or reconstruct those instructions through:
- Direct prompting
- Multi-turn conversations
- Instruction conflicts
- Encoding
- Context manipulation
- Indirect prompt injection
However, security teams should assess the impact of leakage rather than treating every exposed prompt as a critical vulnerability. The real question is whether the information disclosed can help an attacker bypass controls, expose proprietary logic, or facilitate another attack.
6. AI Agent and Tool Security
This is where AI security becomes substantially more consequential. An AI agent may be able to:
- Read emails
- Search internal databases
- Call APIs
- Access cloud resources
- Modify records
- Create tickets
- Send messages
- Execute commands
- Initiate transactions
- Trigger workflows
If an attacker manipulates the agent, the resulting vulnerability may therefore extend far beyond the AI interface. An AI agent security assessment should evaluate:
- Tool permissions
- Least privilege
- Identity and authentication
- Authorization
- Tool-call validation
- Human approval gates
- Privilege escalation
- Agent memory
- Agent-to-agent interactions
- Unauthorized actions
- Monitoring and logging
- Emergency stop mechanisms
OWASP specifically identifies excessive functionality, excessive permissions, and excessive autonomy as core causes of excessive agency.
The principle is simple:
If an AI does not need permission to perform an action, it should not have that permission.
7. API and Integration Security
AI rarely operates alone. Modern AI applications often depend on:
- Model APIs
- SaaS APIs
- Enterprise applications
- Identity providers
- Databases
- Cloud services
- Plugins
- External tools
An AI security assessment should therefore test whether AI-driven workflows can bypass the security controls protecting those systems. Testing can include:
- Authentication bypass
- Authorization failures
- Excessive API privileges
- Insecure tool calls
- Improper input validation
- API parameter manipulation
- Cross-user access
- Cross-tenant access
This is especially important for AI agents because the model may be the decision-making layer while the API remains the actual execution layer.
What Are the Biggest AI Security Risks in 2026?
The AI threat landscape is evolving quickly, but several risks have emerged as particularly important for organizations.
- Prompt injection: Attackers manipulate instructions or external content to alter AI behavior.
- Sensitive information disclosure: AI systems expose information that users should not be able to retrieve.
- Excessive agency: AI systems have excessive permissions, functionality, or autonomy.
- AI supply-chain vulnerabilities: Third-party models, datasets, libraries, plugins, APIs, and AI services introduce security risks.
- Data and model poisoning: Attackers manipulate training, fine-tuning, retrieval, or other data sources to influence AI behavior.
- Insecure output handling: Applications trust AI-generated output without sufficiently validating it before passing it to downstream systems.
- RAG and vector security: Weak authorization or tenant isolation allows attackers to retrieve restricted information.
- Identity and privilege abuse: AI agents operate with permissions that are broader than necessary.
- Unbounded consumption: Attackers abuse AI functionality to drive excessive resource consumption, cost, or availability impact.
These risks increasingly overlap. A single attack can chain several weaknesses together.
For example:
Indirect prompt injection → agent goal manipulation → tool misuse → privilege abuse → sensitive data disclosure
That is why organizations should stop thinking about AI vulnerabilities as isolated “prompt problems.”
The real risk is often the attack chain.
How Is an AI Security Assessment Different From an AI Risk Assessment?
These terms sound similar but answer different questions. An AI risk assessment evaluates the broader risks associated with using AI. It may consider:
- Privacy
- Regulatory requirements
- Business impact
- Operational risk
- Third-party risk
- Human oversight
- Governance
- Transparency
- Accountability
An AI security assessment focuses on whether the AI system and its surrounding technology can be attacked or exploited.
A simple way to remember the difference is:
AI risk assessment: What could go wrong?
AI security assessment: Can an attacker make it go wrong?
Organizations deploying high-impact AI should consider both.
NIST’s AI Risk Management Framework is intended to help organizations manage AI risks across the lifecycle, while NIST’s Generative AI Profile provides additional guidance for generative AI-specific risks. NIST is also developing additional AI RMF guidance for trustworthy AI in critical infrastructure, with a concept note released in April 2026.
Is AI Security Assessment the Same as AI Penetration Testing?
Not necessarily.
AI penetration testing is generally more focused on actively exploiting technical vulnerabilities in an AI application.
An AI security assessment can be broader and may combine:
- Architecture review
- Threat modeling
- AI-specific penetration testing
- LLM security testing
- RAG testing
- Agent security testing
- API testing
- Configuration review
- Access-control testing
- Security-control validation
For an AI-enabled SaaS application, organizations may need both AI-specific testing and conventional application, API, cloud, and network penetration testing. The AI layer should not be tested in isolation if it can reach sensitive enterprise systems.
When Should You Conduct an AI Security Assessment?
Waiting until an AI system reaches production is often too late. Organizations should consider an AI security assessment:
- Before deploying an AI application: Test security controls before customer data and production systems are exposed.
- Before enabling AI agents: The risk profile changes significantly once an AI system can take actions rather than simply generate responses.
- Before connecting sensitive data: RAG, enterprise search, and copilots can expose large volumes of internal information if authorization is poorly implemented.
- Before an enterprise customer security review: Enterprise buyers increasingly want evidence that AI features have been security tested.
- After significant AI architecture changes: Adding a new model, data source, tool, API, agent capability, or third-party AI provider can create new attack paths.
- After an AI security incident: Testing can determine whether related vulnerabilities or attack paths remain exploitable.
- As part of continuous AI security: AI applications change too quickly for a one-time assessment to provide permanent assurance.
What Should an AI Security Assessment Include?
A credible engagement should go beyond automated scanning. A strong methodology generally includes:
- AI architecture discovery: Understand the models, prompts, data flows, RAG components, APIs, tools, agents, identities, and integrations.
- Threat modeling: Identify realistic attack paths based on the AI application’s architecture and business use case.
- AI-specific security testing: Test prompt injection, jailbreaks, information disclosure, RAG security, agent security, tool misuse, and other relevant attack classes.
- Application and API testing: Assess the conventional vulnerabilities surrounding the AI layer.
- Attack-path validation: Determine what an attacker can actually achieve after exploiting an AI weakness.
- Risk prioritization: Separate theoretical model behavior from vulnerabilities with meaningful business impact.
- Remediation guidance: Provide actionable recommendations rather than simply listing vulnerabilities.
- Retesting: Validate whether remediation actually closed the attack path.
This methodology is important because AI security cannot be reduced to a list of prompts.
What Does an AI Security Assessment Report Include?
A useful report should answer the questions that security, engineering, compliance, and leadership teams actually care about:
- What is vulnerable?
- Where is the vulnerability?
- How can it be exploited?
- What data or systems are exposed?
- What permissions can be abused?
- Can the vulnerability be chained with another weakness?
- What is the business impact?
- How severe is the issue?
- How should it be fixed?
- Has the fix been validated?
For example, this:
Prompt injection identified.
is not enough.
A much more useful finding is:
An attacker can inject instructions through externally sourced content, causing the AI agent to access a connected data source using the agent’s privileged identity and return restricted information.
The second finding gives the security team something they can act on.
How Much Does an AI Security Assessment Cost?
There is no universal price for AI security testing. The cost depends on the complexity and attack surface of the AI environment. Factors include:
- Number of AI applications
- Number of models
- AI providers
- RAG architecture
- Vector databases
- AI agents
- Connected tools
- APIs
- Sensitive data
- Number of user roles
- Multi-tenant architecture
- Cloud infrastructure
- Testing methodology
- Black-box or grey-box access
- Red teaming requirements
- Retesting
A public chatbot with no access to sensitive information is fundamentally different from an AI agent that can access customer records and execute privileged API calls. So when comparing AI security assessment companies, do not ask only:
“How much does an AI security assessment cost?”
Ask:
“What attack surface will the provider actually test?”
A low-cost assessment that only tests a handful of prompts may provide very little assurance for a production AI system.
How Do You Choose an AI Security Assessment Company?
The AI security market is growing quickly, but not every provider offering “AI security testing” is testing the same things. Before choosing an AI security assessment company, ask:
Can they test LLM-specific vulnerabilities?
Look for experience with:
- Prompt injection
- Jailbreaks
- Sensitive information disclosure
- System prompt leakage
- Insecure output handling
- Model and data poisoning
Can they test RAG?
Ask whether they evaluate:
- Vector databases
- Retrieval authorization
- Document permissions
- Tenant isolation
- Malicious documents
- Data leakage
Can they test AI agents?
Ask whether testing includes:
- Tool misuse
- Excessive permissions
- Excessive autonomy
- Identity abuse
- Privilege escalation
- Unauthorized actions
- Agent memory
- Human approval controls
Can they test the surrounding application?
An AI vulnerability is much more dangerous when it can be chained with:
- API vulnerabilities
- Broken access control
- Cloud misconfiguration
- Identity weaknesses
- Application vulnerabilities
Your testing provider should therefore understand both AI security and conventional cybersecurity.
Do they demonstrate real attack paths?
Ask to see how findings are documented. The best assessments should demonstrate not only that a weakness exists, but what an attacker can achieve with it.
Which Frameworks Should You Use for AI Security?
Organizations commonly use multiple frameworks and standards because no single framework covers every aspect of AI security.
OWASP Top 10 for LLM Applications
OWASP’s LLM guidance provides a practical foundation for understanding AI-specific application risks such as prompt injection, sensitive information disclosure, supply-chain vulnerabilities, and excessive agency.
OWASP Agentic AI Security
As AI agents become more autonomous, agent-specific risks such as tool misuse, identity and privilege abuse, rogue agents, and human-agent trust exploitation become increasingly important. OWASP’s 2026 exploit roundup has already documented real-world incidents mapped to several of these agentic risks.
NIST AI RMF
The NIST AI RMF provides a broader risk-management framework covering the design, development, deployment, use, and evaluation of AI systems.
NIST Generative AI Profile
NIST AI 600-1 provides additional guidance for identifying and managing risks associated with generative AI. The profile was updated in April 2026.
ISO/IEC 42001
ISO/IEC 42001 addresses AI management systems and organizational AI governance.
However, organizations should understand an important distinction:
Having an AI governance framework does not prove that an AI application is technically secure.
A policy can require protection against prompt injection. Only technical testing can determine whether that protection actually works.
What Is the Difference Between AI Security Testing and AI Governance?
AI governance establishes how an organization manages AI responsibly and securely. AI security testing asks whether those controls withstand an attacker. Think of it this way:
- AI governance: Define the rules.
- AI risk management: Identify what could go wrong.
- AI security assessment: Try to break the system.
- AI red teaming: Simulate realistic adversarial behavior.
- Continuous monitoring: Detect whether the risk changes after deployment.
Organizations that are serious about enterprise AI security need these capabilities to work together.
How Accorian Helps Organizations Test AI Security
Accorian approaches AI security as a cybersecurity problem, not simply an AI governance exercise. Its AI security services cover:
- AI Security Assessments
- AI Chatbot Penetration Testing
- LLM Security Testing
- Prompt Injection Testing
- AI Red Teaming
- Agentic AI Security Assessments
- RAG Security Testing
- AI Threat Modeling
- Third-Party AI Security Validation
- AI Risk Assessments
- AI Governance
- ISO 42001 Advisory
- NIST AI RMF Alignment
The assessment can extend beyond the model to examine the AI application, data, RAG architecture, APIs, identities, tools, integrations, agents, and supporting infrastructure. That matters because the most serious AI vulnerabilities increasingly involve a chain of weaknesses rather than a single prompt.
Accorian’s testing of more than 100 real-world AI chatbots found prompt injection exposure in 82%, internal instruction exposure in 61%, jailbreak bypass in 49%, and PII exposure in 35% of the assessed sample. These figures represent Accorian’s own testing sample and should not be interpreted as a universal prevalence rate across AI applications.
For organizations that need to move from assessment findings to ongoing risk and compliance management, Accorian’s GORICO platform brings AI-enabled governance and compliance workflows together, including centralized management of risks, controls, evidence, and compliance activities.
Is Your AI Secure, or Has It Simply Not Been Tested Yet?
That distinction matters. An AI application can pass functional testing, produce accurate answers, satisfy its business requirements, and still expose sensitive information or provide an attacker with a path into connected systems. The question organizations should be asking in 2026 is no longer:
“Does our AI work?”
It is:
“What happens when someone tries to break it?”
If your organization is deploying an LLM, generative AI application, chatbot, RAG system, copilot, AI-enabled SaaS product, or autonomous AI agent, an AI security assessment can help identify vulnerabilities before they become customer-facing incidents. The earlier those attack paths are found, the more options your security and engineering teams have to fix them.
Don’t wait for your first AI security incident to discover what your AI can access.
Talk to Accorian about an AI security assessment tailored to your AI architecture, data, integrations, permissions, and threat model.
