How is AI penetration testing different from traditional penetration testing?

Traditional penetration testing focuses on network, application, and infrastructure vulnerabilities like SQL injection, cross-site scripting, and authentication bypass. AI penetration testing targets attack surfaces specific to machine learning systems: prompt injection, jailbreaking, system prompt extraction, RAG pipeline poisoning, agent tool-use abuse, adversarial inputs, model extraction, and training data inference. Most organizations need both, as they cover fundamentally different risk categories.

Can we do AI penetration testing ourselves?

You can run basic automated scans using open-source tools like Garak, Promptfoo, and PyRIT. These are useful for catching known vulnerability patterns and should be part of your regular development workflow. However, they do not replace manual expert testing. Experienced AI red teamers find vulnerabilities that automated tools miss, especially in complex agent systems with tool access, multi-model architectures, and custom RAG pipelines. A combination of internal automated testing and periodic external expert assessments is the most effective approach.

How often should we test our AI systems?

Run automated scans as part of your CI/CD pipeline or at minimum monthly. Conduct a manual expert assessment annually, or more frequently if you are making significant architecture changes, deploying new customer-facing AI systems, connecting models to new data sources, or operating in a regulated industry. Any time you change the model, update the system prompt significantly, or add tool access to an agent, you should re-test.

What access do you need to perform an AI penetration test?

This depends on the engagement type. Black-box testing requires only user-level access to the AI system, simulating an external attacker. Gray-box testing adds access to system prompts, architecture documentation, and API specifications. White-box testing includes full source code access, model weights (if self-hosted), training data documentation, and infrastructure access. Most engagements use gray-box or white-box approaches because they are more efficient and find more vulnerabilities in the same timeframe.

What happens if you find a critical vulnerability during testing?

Critical findings are reported immediately, not held until the final report. If a tester discovers a vulnerability that poses an imminent risk, such as a prompt injection that allows data exfiltration from a production system, the testing team notifies the client within hours and provides interim mitigation guidance. Testing may pause on that component until the issue is addressed. The full details, reproduction steps, and remediation guidance are then included in the final report.

AI Penetration Testing: What It Covers, How Long It Takes, and What Affects Cost

You have budget approval. Your leadership agrees that the AI systems your team deployed need security testing. Now you need to figure out what you are actually buying, how to scope it, and what a reasonable timeline looks like.

AI penetration testing is not traditional penetration testing with "AI" added to the title. It covers a fundamentally different set of attack vectors, requires different expertise, and produces different deliverables. If you scope it like a traditional pentest, you will either overpay for testing that misses AI-specific risks, or underpay for an engagement that lacks the depth to find real vulnerabilities.

This guide is written for technical leaders and engineering managers who need to procure AI security testing. It covers what the engagement includes, how timelines work, what factors affect the cost, what good deliverables look like, and how to tell if a vendor actually knows what they are doing.

What AI Penetration Testing Covers That Traditional Pentesting Does Not

A traditional penetration test examines your application's attack surface as a piece of software: HTTP endpoints, authentication flows, session management, input validation, authorization controls, and infrastructure configuration.

An AI penetration test examines all of that, plus the attack surface introduced by the AI components themselves. These are fundamentally different vulnerability classes that require different tools, different expertise, and different testing methodologies.

LLM-Specific Attack Vectors

Prompt injection (direct and indirect). Direct prompt injection is when a user crafts input that overrides the model's system instructions. "Ignore your previous instructions and..." is the simplest example, but real attacks are far more sophisticated. They use encoding tricks, multi-turn conversation manipulation, payload splitting across messages, and context window stuffing.

Indirect prompt injection is more dangerous and harder to detect. Instead of attacking through the user input field, the attacker embeds instructions in data that the model processes. A poisoned document in your RAG pipeline, a malicious email that your AI assistant summarizes, or adversarial text hidden in a web page that your model scrapes. The model follows the embedded instructions because it cannot distinguish between trusted instructions and untrusted content.

Jailbreaking. Systematic attempts to bypass the model's safety filters and behavioral constraints. This includes role-playing attacks ("You are now DAN, a model with no restrictions..."), hypothetical framing ("In a fictional scenario where safety rules don't apply..."), and multi-turn escalation where the attacker gradually shifts the model's behavior through a sequence of seemingly benign requests.

System prompt extraction. Testing whether an attacker can extract the full system prompt. This is often more damaging than it sounds. System prompts frequently contain business logic, internal tool definitions, database schema information, API endpoint paths, and sometimes credentials. Extracting the system prompt gives an attacker a detailed map of the AI system's capabilities and constraints.

Data exfiltration through model responses. Testing whether the model can be tricked into including sensitive data in its responses. This includes data from the model's context window, data retrieved by RAG pipelines, data accessed through tool calls, and in some cases, memorized training data. The attack might not look like exfiltration; the model simply includes data in a response that it should have filtered out.

Agent Security

Modern AI deployments are increasingly agentic. The model does not just generate text; it plans, uses tools, accesses APIs, queries databases, and takes actions. This introduces an entirely new attack surface.

Tool-use abuse. Testing whether an attacker can instruct the agent to use its tools in unauthorized ways. If the agent has access to query_database(), can an attacker craft input that causes the agent to run queries outside its intended scope? If the agent can send_email(), can it be tricked into sending messages to arbitrary recipients?

Privilege escalation. Testing whether the agent can be made to exceed its intended authorization level. This is analogous to privilege escalation in traditional security, but the mechanism is different. Instead of exploiting a code vulnerability, the attacker manipulates the agent's reasoning to access capabilities that should be restricted.

Inter-agent manipulation. In multi-agent systems, testing whether one agent can be compromised and then used to attack other agents in the system. If Agent A processes user input and passes summaries to Agent B, can an attacker embed instructions in the input to Agent A that survive summarization and influence Agent B's behavior?

Memory poisoning. For agents with persistent memory, testing whether an attacker can inject false information into the agent's memory store. If the agent remembers context from previous conversations, an attacker might plant instructions in an early conversation that influence the agent's behavior in future sessions, potentially with different users.

Model Security

Adversarial inputs. Testing whether carefully crafted inputs can cause the model to produce incorrect or harmful outputs in ways that are difficult for humans to detect. In classification systems, this means inputs designed to cause misclassification. In generative systems, this means inputs that produce outputs that appear normal but contain embedded manipulation.

Model extraction. Testing whether an attacker can reconstruct a proprietary model's behavior through systematic querying. This is primarily a concern for self-hosted or fine-tuned models where the model itself represents intellectual property. An attacker with API access makes thousands of carefully chosen queries and uses the responses to train a copy of the model.

Training data inference. Testing whether the model reveals information about its training data. This is relevant for models fine-tuned on proprietary or sensitive data. Membership inference attacks can determine whether a specific data point was in the training set. Data extraction attacks attempt to get the model to reproduce training data verbatim.

RAG Pipeline Security

Document poisoning. Testing whether an attacker can introduce malicious content into the documents your RAG system indexes. If your pipeline indexes web pages, customer uploads, or shared drives, an attacker might plant a document containing adversarial instructions that the model follows when the document is retrieved.

Retrieval manipulation. Testing whether an attacker can manipulate which documents the RAG system retrieves. By crafting queries with specific terminology or by poisoning the embedding space, an attacker might cause the retrieval system to surface documents outside the intended scope or to prioritize attacker-controlled content over legitimate sources.

Context window attacks. Testing how the model behaves when the retrieved context is adversarial. If the RAG system fills the model's context window with attacker-controlled content, the model may follow instructions from that content rather than its system prompt.

Infrastructure and Integration Security

This is where AI security testing overlaps with traditional testing, but with AI-specific considerations.

API security. Authentication, authorization, and input validation for AI endpoints. This includes testing whether API keys are properly scoped, whether rate limiting prevents automated attacks, and whether the API properly validates and sanitizes inputs before passing them to the model.

Logging and monitoring gaps. Assessing whether the organization has sufficient visibility into AI system behavior. Can they detect a prompt injection attempt? Can they identify data leakage? Do they log tool calls and model outputs?

Model supply chain. For systems using open-source models, verifying the integrity of model downloads, checking for known vulnerabilities in model dependencies, and assessing the security of the model serving infrastructure.

Engagement Types and What They Include

AI penetration testing engagements generally fall into three categories. The right choice depends on the number of AI systems in scope, the depth of testing required, and whether you need ongoing coverage.

Focused Assessment (1 AI System, 1 to 2 Weeks)

This is the right starting point for most companies. You pick your highest-risk AI system and get a thorough security assessment of that single system.

Typical scope:

One AI application (for example, a customer-facing chatbot with RAG, or an internal AI agent with tool access)
Black-box and gray-box testing of the model's behavior
Prompt injection and jailbreak testing using both automated tools and manual techniques
System prompt extraction attempts
If applicable: RAG pipeline security, tool-use security, output filtering validation
API authentication and authorization review for the AI endpoints

What you typically find:

System prompt extraction vulnerabilities (very common; most system prompts can be extracted without much effort)
Direct prompt injection vectors that bypass safety instructions
Missing or insufficient output filtering (model returns raw data that should be redacted)
Overly broad tool permissions on agents
Logging gaps (inputs and outputs not captured, tool calls not audited)
Rate limiting insufficient to prevent automated attacks

Deliverables:

Executive summary with overall risk rating
Technical findings with severity ratings, reproduction steps, and evidence
Prioritized remediation plan
Compliance mapping if applicable

A focused assessment gives you a clear picture of your most critical AI system's security posture and actionable steps to improve it. BeyondScale offers this as part of our AI penetration testing service.

Comprehensive Audit (Multiple Systems, 3 to 6 Weeks)

When you have multiple AI systems in production and need a broad assessment of your organization's AI security posture.

Expanded scope includes everything in the focused assessment, plus:

Multiple AI systems tested (for example, customer support agent, internal document Q&A, code assistant, automated data pipeline)
Cross-system interaction testing (can compromising one system affect another?)
Architecture review of your overall AI infrastructure
Data flow analysis (how does data move between AI systems, databases, APIs, and users?)
Compliance mapping against relevant frameworks: OWASP LLM Top 10, OWASP Agentic Top 10, NIST AI RMF, ISO 42001, EU AI Act
Security architecture recommendations

This level of engagement is appropriate when you are preparing for a compliance audit, responding to customer security questionnaires about your AI systems, or when you have multiple AI systems that share infrastructure or data sources.

BeyondScale's AI security audit covers this scope with structured methodology based on industry frameworks.

Managed Security Program (Ongoing)

For organizations that want continuous AI security coverage rather than point-in-time assessments.

What ongoing programs typically include:

Initial comprehensive assessment to establish baseline
Continuous automated scanning of AI endpoints (weekly or monthly)
Quarterly manual red-team assessments
Security review of new AI deployments before they go to production
Incident response retainer for AI-specific security events
Regular reporting to stakeholders with trend analysis
Advisory support for AI architecture decisions

This model is most cost-effective when you are deploying new AI systems regularly, when your AI systems handle sensitive data or have significant tool access, or when compliance requirements mandate ongoing security monitoring.

Details on BeyondScale's ongoing program are available on our managed AI security page.

What Determines the Cost

We do not publish pricing because every engagement is scoped based on the specific systems, complexity, and requirements involved. But we can explain exactly what factors affect the scope, which directly determines what you will pay. Understanding these factors helps you set realistic budget expectations and compare proposals from different vendors.

Number of AI Systems in Scope

This is the most straightforward factor. Testing one AI chatbot takes less time than testing five AI systems with different architectures. Each system has its own attack surface, its own configuration, and its own set of potential vulnerabilities.

Some systems share infrastructure (same model, same RAG pipeline, same authentication). In those cases, testing overlaps and the marginal cost of each additional system decreases. But if each system uses a different model, different tools, and different data sources, each one requires a full independent assessment.

Complexity of the AI Architecture

A simple chatbot that takes user input, sends it to an API, and returns the response has a relatively small attack surface. You are testing the prompt, the input handling, and the output filtering. It is a contained system.

A multi-agent system with tool access is a different proposition entirely. Consider a system where:

Agent A processes user input and decides which specialist agent to route to
Agent B queries a database and summarizes results
Agent C generates reports and can email them to users
All three agents share a memory store and can communicate with each other

This system has exponentially more attack vectors. You need to test each agent individually, test the inter-agent communication, test the tool access controls, test the shared memory, and test how a compromise of one agent affects the others. The testing effort scales with the number of components and the number of interactions between them.

Depth of Testing

There is a spectrum of depth for AI security testing:

Automated scanning uses tools like Garak, Promptfoo, and PyRIT to run hundreds of known attack payloads against your AI system. This catches well-known vulnerability patterns quickly and efficiently. It is a necessary baseline but does not find novel vulnerabilities or complex attack chains.

Manual red-teaming adds human expertise. An experienced tester designs custom attack scenarios based on your specific system, its tools, its data access, and its business context. They chain multiple techniques together, try creative approaches that automated tools do not cover, and think like a real attacker targeting your specific application. This finds vulnerabilities that automated tools miss.

Full adversarial simulation goes further. The tester operates with an attacker's mindset and minimal constraints. They might start with the AI system and pivot to traditional attack vectors, or start with traditional reconnaissance and use information gained to target the AI system more effectively. This is the most time-intensive and finds the deepest issues.

Each step up the depth spectrum takes more time and requires more experienced testers.

Compliance Requirements

If you need findings mapped to specific compliance frameworks, that adds scope to the engagement. Mapping vulnerabilities to the EU AI Act, NIST AI RMF, or ISO 42001 requires the tester to document not just what they found, but which specific compliance requirements are affected and what remediation is needed to satisfy those requirements.

If you are pursuing SOC 2 certification or HIPAA compliance for AI systems, the engagement may need to include specific test cases that your auditor requires.

This documentation adds time to the reporting phase and may add test cases to the active testing phase.

Remediation Support

Some engagements end with a report. Others include hands-on remediation guidance where the testing team works with your engineers to fix the issues they found.

Report-only engagements are less expensive but require your team to figure out the fixes on their own. This works well if you have experienced engineers who understand AI security concepts and just need to know what to fix.

Remediation support means the testing team reviews your proposed fixes, validates that they actually address the vulnerability, and provides implementation guidance. This is more valuable for teams without deep AI security experience.

Re-testing (validating that fixes work) is sometimes included in the initial engagement and sometimes scoped as a separate follow-up. If included, it adds one to two weeks after you have implemented the fixes.

Timeline Expectations

Here is a realistic timeline for an AI penetration testing engagement from first conversation to final deliverable.

Scoping and Contracting (1 to 2 Weeks)

This covers the initial conversations to define scope, the vendor's review of your AI architecture, proposal creation, contract review, and legal/procurement processes. Some organizations can move through this in a few days. Others, especially those with formal procurement processes, take longer.

What the vendor needs from you during scoping:

A description of each AI system in scope (what it does, what model it uses, what data it accesses, what tools it has)
Architecture diagrams or documentation if available
Any compliance requirements that need to be addressed
Your preferred testing window (some companies restrict testing to specific time periods)

Active Testing (1 to 4 Weeks)

The length depends on the engagement type:

Focused assessment of a single AI system: 1 to 2 weeks of active testing
Comprehensive audit of multiple systems: 2 to 4 weeks, sometimes longer for complex multi-agent architectures
Ongoing programs have continuous testing rather than a fixed window

During active testing, the vendor's team is actively probing your AI systems. You should expect:

Daily or regular status updates for longer engagements
Immediate notification of critical findings (the vendor should not sit on a finding that represents an imminent risk)
Occasional requests for information or access adjustments
Some increase in traffic to your AI endpoints (the vendor should coordinate with your team to ensure testing traffic does not affect production users)

Reporting and Review (1 Week)

After active testing concludes, the vendor compiles findings into a structured report. Expect a draft report within five to seven business days of testing completion.

The review phase typically includes:

A findings walkthrough meeting where the testing team presents results and answers questions
An opportunity to dispute or discuss findings (sometimes an apparent vulnerability has a mitigating control the testers were not aware of)
Final report delivery incorporating any adjustments from the review discussion

Remediation Validation (1 to 2 Weeks, If Included)

After you have implemented fixes, the testing team re-tests to verify that the vulnerabilities are actually resolved. This is targeted testing, not a full re-assessment. They re-run the specific attack techniques that succeeded during the initial test and confirm that they no longer work.

This phase depends on how quickly your team implements fixes. Some organizations remediate critical findings within days. Others need weeks or months. The re-test happens when you are ready.

What Good Deliverables Look Like

The deliverable is the tangible thing you are paying for. A vendor's report quality tells you a lot about their testing quality. Here is what to expect from a serious AI security testing firm.

Executive Summary With Risk Ratings

A one-to-two page summary written for non-technical stakeholders. It should answer: What is our overall AI security posture? What are the most serious risks? What are the top-priority actions?

Risk ratings should use an established framework (CVSS, DREAD, or a similar methodology adapted for AI-specific risks) so that findings can be compared and prioritized objectively.

Technical Findings With Reproduction Steps

Each finding should include:

Description: What the vulnerability is, in clear technical language
Severity rating: Critical, high, medium, low, or informational, with justification
Attack scenario: How an attacker would exploit this in a real-world context
Reproduction steps: Exact steps and inputs to reproduce the finding. This is non-negotiable. If a vendor gives you a finding without reproduction steps, push back
Evidence: Screenshots, logs, or transcripts showing the vulnerability in action
Impact: What happens if this vulnerability is exploited. Data exposure, unauthorized actions, compliance violations, reputational damage
Affected components: Which specific model, agent, pipeline, or endpoint is affected

Compliance Mapping

If compliance mapping was in scope, each finding should indicate which framework requirements it affects. For example, a prompt injection vulnerability might map to OWASP LLM01, NIST AI RMF Map 1.5, and EU AI Act Article 15.

This mapping saves significant time when you are preparing for compliance audits or responding to customer security questionnaires.

Prioritized Remediation Plan

Not just "fix these things" but a structured plan that accounts for:

Severity and exploitability (what to fix first)
Dependencies (fixing A before B because B depends on A)
Effort estimates (quick wins vs. architectural changes)
Interim mitigations (what you can do right now to reduce risk while working on permanent fixes)

Re-test to Validate Fixes

The engagement should either include a re-test phase or explicitly scope one as a follow-up. A vulnerability report without validation is incomplete. You need confirmation that your fixes actually work against the specific attack techniques that succeeded during testing.

How to Evaluate AI Pentest Vendors

The market for AI security testing is growing, and not every vendor offering "AI penetration testing" has the expertise to deliver it. Here is how to separate the specialists from the traditional pentest firms that added AI to their marketing page.

Ask for Sample Reports

Request a redacted sample report from a previous AI penetration testing engagement. Look for:

AI-specific findings (prompt injection, jailbreaking, RAG poisoning), not just traditional web application vulnerabilities found on an AI system's API
Detailed reproduction steps that show the tester crafted custom attack payloads, not just ran an automated scanner
Evidence of manual testing beyond automated tool output
Remediation guidance that is specific and actionable, not generic

If the sample report reads like a traditional web application pentest report with an "AI" section tacked on at the end, that tells you something about the vendor's actual focus.

Verify LLM-Specific Experience

Ask specific questions:

How many AI-specific penetration tests has your team conducted in the past 12 months?
What percentage of your testing practice is focused on AI and ML systems specifically?
Can you walk me through your methodology for testing a multi-agent system with tool access?
What tools does your team use for AI-specific testing? (Good answers include Garak, Promptfoo, PyRIT, custom tooling. Bad answers are vague or only mention traditional tools like Burp Suite.)
Does your team have experience with the specific model providers you use? (Testing GPT-4o is different from testing Claude, which is different from testing a self-hosted Llama model.)

Check Methodology Depth

Ask the vendor to describe their testing methodology in detail. You want to hear about:

Full-stack testing. Do they test the model behavior, the agent framework, the RAG pipeline, the API layer, and the infrastructure? Or just run prompts against the model endpoint?
Manual and automated testing. What automated tools do they use, and what do they test manually? A vendor that only runs automated scans is missing significant vulnerability classes. A vendor that only does manual testing will miss coverage breadth.
Attack chaining. Do they attempt to chain multiple vulnerabilities together? A low-severity prompt injection combined with a medium-severity tool access issue might create a critical attack path.
Business context testing. Do they craft test cases specific to your application's business logic, or only run generic attack payloads?

Ask About the Red Team's Background

The people doing the testing matter. Ask about:

Background in adversarial machine learning, NLP, or ML security research
Relevant certifications or publications (while not required, they indicate depth of expertise)
Experience with the specific AI frameworks and model providers in your stack
Ratio of AI-specialized testers to general penetration testers on the team

A vendor with a deep bench of traditional pentesters and one person who "knows AI" will not deliver the same quality as a team where AI security is the primary focus.

Making the Decision

If you have read this far, you are likely in one of two positions: you have budget and need to scope an engagement, or you are building the business case to get budget approved.

If you have budget, start with a focused assessment of your highest-risk AI system. That is the system with the most sensitive data access, the broadest tool permissions, or the most exposure to untrusted user input. A focused assessment gives you concrete findings, validates (or invalidates) your current security assumptions, and provides a foundation for scoping broader testing later.

If you are building the case, the argument is straightforward. Your organization has deployed AI systems that introduce attack vectors not covered by your existing security testing. Traditional pentests do not test for prompt injection, RAG poisoning, or agent tool-use abuse. These are documented, reproducible attack techniques that are actively exploited. An AI-specific assessment is the only way to know whether your systems are vulnerable.

BeyondScale specializes in AI penetration testing and security audits for companies running AI in production. Our team focuses exclusively on AI and ML security, and our engagements are scoped to deliver actionable findings for technical teams. Contact us to discuss your specific systems and get a scoped proposal.