Your AI application may be working exactly as designed. That does not mean it is secure. A chatbot can expose sensitive information. A RAG application can retrieve documents a user should never see. An AI agent can be manipulated into calling a privileged API. A prompt injection can become a data-exfiltration path. And these vulnerabilities may not be discovered by a conventional application penetration test alone.
An AI security assessment tests what happens when someone deliberately tries to make your AI behave in ways it was never supposed to.
For organizations deploying LLMs, AI chatbots, RAG applications, copilots, and AI agents, that question is becoming critical before production, not after an incident. This guide explains what an AI security assessment covers, what LLM penetration testing actually tests, how prompt injection and RAG security are assessed, when you need AI red teaming, what testing can cost, and how to choose an AI security testing provider.
What Is an AI Security Assessment?
An AI security assessment evaluates whether an AI system, its data, applications, APIs, integrations, models, and supporting infrastructure can be attacked, manipulated, or misused. Depending on the architecture, an assessment can test:
- Prompt injection and jailbreaks
- Sensitive information disclosure
- System prompt leakage
- RAG and vector database security
- AI agent and tool security
- Authentication and authorization
- API and integration security
- Data and model poisoning
- Insecure output handling
- Excessive permissions and agency
- AI supply-chain risks
- Cloud and application security
The core question is simple:
‘Can an attacker manipulate the AI to cross a security boundary?’
That could mean accessing another customer’s data, retrieving an internal document, bypassing authorization, invoking a privileged tool, or triggering an unauthorized business action.
A strong AI security assessment therefore looks beyond whether an AI produces an unsafe response. It tests the attack path and its real-world impact.
Why Is AI Security Testing Different From Traditional Penetration Testing?
Traditional penetration testing remains essential. Your AI application still has APIs, authentication, databases, cloud infrastructure, web interfaces, and third-party integrations. But AI introduces another attack surface:
Prompts → Model → Data → RAG → APIs → Tools → Agents → Business systems
An attacker may manipulate one component to compromise another.
For example:
Prompt injection → manipulated retrieval → unauthorized document access → sensitive data exposure
Or:
Indirect prompt injection → agent manipulation → privileged API call → unauthorized action
This is why organizations should not treat AI security testing as a replacement for conventional penetration testing.
AI-specific testing and traditional application/API security testing need to work together.
What Should an AI Security Assessment Test?
A comprehensive assessment should be based on the actual architecture rather than a generic checklist.
1. Prompt Injection and Jailbreaks
Can an attacker manipulate the AI into ignoring its intended instructions?
Prompt injection testing evaluates whether malicious instructions can override, conflict with, or manipulate the application’s intended behavior. Testing can include:
- Direct prompt injection
- Indirect prompt injection
- Jailbreaks
- Instruction manipulation
- System prompt extraction
- Malicious content in retrieved sources
Prompt Injection is LLM01:2025 in the current OWASP Top 10 for LLM and GenAI applications. OWASP also identifies sensitive information disclosure, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, and unbounded consumption among the major risks.
2. Sensitive Information Disclosure
Can your AI reveal information that a user is not authorized to access?
Security testing should determine whether an attacker can extract:
- PII
- Customer information
- Financial data
- Healthcare information
- Credentials and secrets
- Internal documents
- Source code
- System instructions
- Other users’ conversations
For SaaS applications, this becomes particularly important when multiple customers share infrastructure, databases, or retrieval systems. A vulnerability that exposes another tenant’s data is not simply an AI-quality issue. It is a security boundary failure.
3. RAG and Vector Database Security
Can your AI retrieve information the user should never receive?
Retrieval-Augmented Generation introduces additional data and retrieval layers that need to be secured. OWASP’s current guidance identifies vector and embedding weaknesses as a significant risk for RAG systems, including unauthorized access, data leakage, cross-context information leaks, embedding attacks, and data poisoning. A RAG security assessment should examine:
- Retrieval authorization
- Tenant isolation
- Vector database permissions
- Sensitive information retrieval
- Malicious documents
- Data poisoning
- Embedding weaknesses
- Access-control enforcement
The critical rule is:
Authorization should not depend on the LLM.
If a user cannot access a document, application and data-layer controls should prevent the document from being retrieved in the first place.
4. AI Agents and Excessive Agency
What happens if an attacker manipulates your AI into taking an action?
This is where AI security becomes significantly more consequential. An AI agent may be able to:
- Call APIs
- Query databases
- Search knowledge bases
- Modify records
- Send messages
- Execute workflows
- Access cloud resources
- Invoke external tools
OWASP identifies Excessive Agency as a major LLM risk arising from excessive functionality, permissions, or autonomy. The potential impact depends on the systems and tools the AI can access. Testing should therefore ask:
- What can the agent access?
- What can it change?
- What permissions does it have?
- Can prompt injection trigger those actions?
- Are high-impact actions independently authorized?
For an agent, the security question is no longer just:
“Can the model be manipulated?”
It is:
“What can the attacker make the model do?”
What Is LLM Penetration Testing?
LLM penetration testing is an offensive security assessment designed to identify and validate exploitable vulnerabilities in an LLM-powered application. It can include:
- Prompt injection
- Jailbreak testing
- System prompt extraction
- Sensitive data extraction
- RAG manipulation
- Tool abuse
- API attacks
- Authorization bypass
- Insecure output handling
- AI-specific attack chains
The objective is to demonstrate what an attacker can actually achieve.
For example, reporting:
“Prompt injection vulnerability identified.”
is less useful than demonstrating:
Prompt injection → restricted RAG content → unauthorized customer data exposure
The second finding gives the security and engineering teams a clear attack path, business impact, and remediation priority.
What Is Prompt Injection Testing?
Prompt injection testing evaluates whether an attacker can manipulate an AI application through malicious instructions.
Direct prompt injection
The attacker directly interacts with the model and attempts to:
- Override instructions
- Bypass safeguards
- Extract hidden information
- Change the AI’s intended behavior
- Trigger unauthorized actions
Indirect prompt injection
The malicious instruction is introduced through content the AI later processes.
For example:
Malicious document → RAG retrieval → AI processes hidden instruction → unintended behavior
This matters for systems processing:
- Documents
- Emails
- Web pages
- Support tickets
- Knowledge bases
- Uploaded files
- Third-party content
For AI applications that consume external information, testing only the user’s prompt is not enough.
How Do You Test an AI Chatbot for Security Vulnerabilities?
An AI chatbot security assessment should test both the AI and the application surrounding it. A typical engagement can include:
- Planning: Understand architecture, functionality, data, integrations, and testing boundaries.
- Reconnaissance: Identify endpoints, APIs, services, and attack surfaces.
- AI-specific testing: Test prompt injection, jailbreaks, data leakage, system prompt exposure, and other LLM risks.
- Application/API testing: Test authentication, authorization, session security, APIs, and conventional vulnerabilities.
- Exploitation: Validate whether vulnerabilities can produce meaningful security impact.
- Reporting: Provide evidence, severity, attack paths, reproduction steps, and remediation guidance.
Accorian’s current AI chatbot penetration-testing methodology includes planning, reconnaissance, vulnerability assessment, prompt injection testing, LLM-specific checks, exploitation, privilege escalation, and reporting.
What Is AI Red Teaming?
AI red teaming is adversarial testing designed to simulate realistic attacks against an AI system and identify weaknesses that may emerge when multiple vulnerabilities are combined. It can cover:
- Prompt injection
- Jailbreaks
- Data exfiltration
- RAG manipulation
- Tool misuse
- Agent manipulation
- Privilege escalation
- Business workflow attacks
- Multi-step attack chains
AI red teaming becomes particularly valuable when an AI system can influence sensitive business processes or interact with enterprise systems. This is increasingly relevant for agentic AI. OWASP’s 2026 exploit reporting has documented real-world cases involving excessive agency, tool misuse, privilege abuse, and sensitive information exposure.
AI Security Assessment vs. LLM Penetration Testing vs. AI Red Teaming
These terms are related, but they are not interchangeable.
- AI Security Assessment: Broad evaluation of the AI environment, including architecture, AI-specific vulnerabilities, data, integrations, applications, APIs, and controls.
- LLM Penetration Testing: Offensive testing focused on identifying and validating exploitable vulnerabilities in an LLM-powered application.
- AI Red Teaming: Adversarial testing focused on realistic attacker objectives and multi-step attack paths.
Your AI architecture and risk profile should determine which approach you need.
What Is the Difference Between an AI Security Assessment and an AI Risk Assessment?
An AI security assessment asks:
‘Can this AI system be attacked, exploited, or manipulated?’
An AI risk assessment asks:
‘What risks does this AI create for the organization, customers, data, operations, and compliance posture?’
An AI risk assessment can identify prompt injection or data leakage as significant risks. An AI security assessment can then test whether those vulnerabilities are actually exploitable. Accorian similarly distinguishes AI security assessments, which focus on technical vulnerabilities and attack paths, from AI risk assessments, which address broader privacy, regulatory, operational, ethical, business, and governance risks.
For mature AI programs, the two can work together:
AI Risk Assessment → Security Controls → Technical Testing → Remediation → Continuous Monitoring
How Much Does an AI Security Assessment Cost?
There is no universal price for AI security testing. Cost depends on the scope and complexity of the AI environment, including:
- Number of AI applications
- Models and providers
- RAG architecture
- APIs and integrations
- AI agents
- Connected tools
- Data sensitivity
- User roles
- Application complexity
- Testing depth
- Red teaming requirements
- Retesting
A simple public chatbot and an autonomous enterprise AI agent should not have the same testing scope. When comparing providers, ask:
What exactly is included in the assessment?
A documentation review is not equivalent to an expert-led technical assessment that attempts to exploit the AI application.
How Long Does an AI Security Assessment Take?
There is no universal timeline. A focused AI application assessment may require significantly less effort than an enterprise environment involving multiple models, RAG, agents, APIs, sensitive data, and business-critical workflows.
Instead of asking only:
“How many days will testing take?”
ask:
“Which attack surfaces and vulnerabilities will actually be tested?”
The depth of coverage matters more than an arbitrary number of assessment days.
When Should You Conduct an AI Security Assessment?
Before production is the most important point.
But AI security testing should not end at launch. Consider reassessment when you:
- Deploy a new AI application
- Release a major LLM feature
- Change model providers
- Introduce RAG
- Add an AI agent
- Connect a new API or tool
- Change permissions
- Add sensitive data
- Make a significant architecture change
- Experience a security incident
AI systems evolve quickly. A new model, integration, data source, prompt architecture, or agent capability can create a new attack path.
Treat AI security testing as part of the AI lifecycle, not a one-time checkbox.
How Do You Choose an AI Security Testing Company?
The right AI security testing company should be able to test both the AI-specific attack surface and the conventional security controls surrounding it. Before choosing a provider, ask:
Does the provider test AI-specific vulnerabilities?
Look for experience with:
- Prompt injection
- Jailbreaks
- LLM security
- RAG security
- AI agents
- Tool misuse
- System prompt leakage
- Data leakage
Does the provider test the surrounding application?
AI testing should not happen in isolation from:
- APIs
- Authentication
- Authorization
- Cloud infrastructure
- Databases
- Identity
- Third-party integrations
Does the provider demonstrate real exploitation?
Ask whether findings include:
- Evidence
- Attack paths
- Reproduction steps
- Business impact
- Remediation
- Retesting
Does the provider understand your use case?
Testing a public chatbot is different from testing an AI agent connected to customer databases.
Your testing methodology should be based on your architecture, not a generic AI checklist.
What Are AI Security Assessments Finding in Real-World Applications?
First-party testing data can provide a useful indication of the types of weaknesses organizations may encounter. Accorian has tested more than 100 real-world AI chatbots and identified:
- 82% with prompt injection exposure
- 61% with internal instruction exposure
- 49% with jailbreak bypass
- 35% with PII exposure
These figures represent Accorian’s assessed sample, not universal prevalence across all AI applications.
The important point is not that every AI application has these vulnerabilities.
It is that organizations should validate their own security controls instead of assuming that AI guardrails are sufficient.
Accorian’s current AI-security material also highlights data exposure, privilege escalation, and multi-tenancy as areas requiring particular attention in AI-driven products.
What Should SaaS Companies Test Before Launching an AI Feature?
If you’re launching an LLM-powered SaaS feature, chatbot, copilot, or RAG application, your assessment should consider at least six areas.
- LLM security: Prompt injection, jailbreaks, system prompt leakage, sensitive information disclosure.
- RAG security: Retrieval authorization, tenant isolation, vector database security, malicious documents, poisoning.
- AI agent security: Tool permissions, API access, excessive agency, privilege boundaries, high-impact actions.
- Application security: Authentication, authorization, APIs, sessions, injection vulnerabilities.
- Data security: PII, customer data, credentials, internal documents, secrets.
- Operational security: Logging, monitoring, abuse detection, rate limiting, and incident response.
If your AI can see it, retrieve it, call it, modify it, or act on it, it belongs in your security conversation.
What Happens If You Don’t Test Your AI?
The biggest mistake is assuming:
“It’s only an AI chatbot.”
That assumption becomes harder to defend when the chatbot can access customer records, internal documents, APIs, databases, or business workflows.
A seemingly simple attack can become:
- Prompt injection
- Unauthorized instruction
- Restricted data retrieval
- API/tool abuse
- Privilege escalation
- Business impact
The risk is not simply that an AI gives a bad answer.
The risk is that an attacker finds a path from the AI interface into something valuable.
How Can Accorian Help With AI Security Testing?
Accorian’s AI security practice combines AI-specific testing with broader cybersecurity assessment capabilities. Services include:
- AI Security Assessments
- AI Chatbot Penetration Testing
- LLM Security Testing
- Prompt Injection Testing
- AI Threat Modeling
- AI Red Teaming
- Agentic AI Security Assessments
- Third-Party AI Security Validation
- AI Vendor Risk Assessment
- HITRUST AI Security
- NIST AI RMF alignment
- ISO 42001 advisory
- AI security governance
Accorian’s current AI security offering is designed to address in-house models, third-party AI tools, and open-source LLM environments, while connecting AI security with risk and compliance activities.
And for organizations that need to operationalize what comes after the assessment, GORICO, Accorian’s AI-enabled GRC platform, can centralize controls, evidence, risks, and compliance workflows.
GORICO supports capabilities including AI-powered policy and procedure generation, AI-assisted risk assessment population, evidence mapping, and AI policy and procedure validation. Its current platform also supports multi-framework compliance and continuous security workflows.
Is Your AI Actually Secure?
You don’t know because the model gives good answers. You know because you’ve tested what happens when someone tries to make it:
- Reveal sensitive information
- Bypass authorization
- Ignore its instructions
- Poison its knowledge
- Cross tenant boundaries
- Invoke unauthorized tools
- Abuse APIs
- Escalate privileges
- Trigger unintended business actions
AI security is not about assuming your guardrails work. It’s about testing what happens when someone deliberately tries to break them.
If your organization is deploying an LLM, AI chatbot, RAG application, copilot, or AI agent, now is the time to understand the attack surface.
Get Your AI Security Assessment Scope
Tell Accorian what you’re deploying and what you need to validate:
AI Security Assessment | LLM Penetration Testing | Prompt Injection Testing | RAG Security | AI Agent Security | AI Red Teaming
Before an attacker discovers what your AI can access, find out yourself.
