Andersen applies offensive security testing across AI applications, infrastructure, and high-risk integrations for enterprise clients.
AI Penetration Testing
Andersen performs AI penetration testing for LLM apps, chatbots, and AI agents before attackers exploit them. We map each issue to clear remediation guidance and validate every finding against real attack paths. 300+ security projects delivered across regulated environments.
Proven expertise in AI pentesting
Certified engineers and security researchers validate findings and map issues to real exploitation paths before remediation.
Our teams protect public and private AI systems across finance, healthcare, SaaS, and regulated operations.
AI penetration testing services we provide
We deliver AI pentesting for web apps with chatbots and AI agents. We identify prompt injection, unsafe automation, and sensitive data leakage before attackers exploit them, protecting your attack surface and customer trust.
Client outcome:
- Confident protection against prompt injection and abuse;
- Reduced security risk from web-exposed AI workflows;
- Clear remediation guidance and real attack evidence.
Andersen performs AI pentesting for public APIs and portals. We simulate externally reachable abuse scenarios to show how attackers could extract sensitive data or misuse exposed services before a breach.
Client outcome:
- Evidence of externally reachable abuse scenarios;
- Clear priorities for public API controls;
- Faster action before public security incidents.
Our security experts run AI pentesting for internal AI agents and GenAI tools. We assess privilege boundaries and prompt handling to prevent unauthorized AI actions and data exposure.
Client outcome:
- Reduced risk of AI agents bypassing access controls;
- Clear boundaries between user roles and data access;
- Actionable fixes for internal security weaknesses.
We test AI APIs for model endpoints and integrations, assessing authentication and tool orchestration for exploitable abuse. The assessment reduces unverified findings and clarifies your security posture.
Client outcome:
- Exploitable API abuse scenarios confirmed;
- Fewer inaccurate findings through expert human validation;
- Actionable security testing results for engineering teams.
Andersen delivers continuous AI security testing for rapid releases. We monitor environment changes and detect emerging threats so your security posture reflects evolving AI workflows.
Client outcome:
- Continuous visibility into release-driven security changes;
- Faster detection of emerging threats after each release;
- Security posture tracking through rapid AI iteration.
We apply AI-powered dynamic application security testing to identify exploitable vulnerabilities. Combining security testing tools with human pentesters confirms technical impact and reduces false positives.
Client outcome:
- Real, exploitable vulnerabilities identified with evidence;
- Confirmed technical impact from dynamic testing;
- Prioritized engineering recommendations for each finding.
Start your AI pentesting program before attackers find critical vulnerabilities
Certifications and security standards
Andersen validates AI pentesting work through accredited security talent, controlled delivery processes, and standards-aligned governance for sensitive environments.
Benefits of AI penetration testing
Early detection of AI-specific vulnerabilities
Andersen identifies prompt injection, jailbreaks, and retrieval abuse before release, giving security teams verified issues to address.
AI regulatory and compliance readiness
Andersen provides evidence-backed reports that help security teams prepare for audits and sensitive data reviews.
Protection of sensitive data and user trust
Andersen traces data across trust boundaries and verifies that model controls prevent unauthorized disclosure.
AI vulnerabilities we uncover
Andersen tests AI applications for exploitable weaknesses. A pentest does not audit model bias or long-term data drift; use AI red teaming for broader behavioral risk.
Prompt injection uses direct user input or indirect content from connected data sources to override instructions, reveal hidden context, or bypass guardrails. Andersen checks whether insecure output handling lets an attacker turn that response into unauthorized access or action.
What this can lead to:
- Guardrail bypasses and unsafe model behavior;
- Unauthorized actions through prompt manipulation;
- Increased exposure of internal logic or sensitive context.
Sensitive data leakage occurs when a model reveals training data, retrieved documents, or connected-system context through a prompt or response. Andersen traces these disclosure and data exfiltration paths to identify where access controls fail.
Common attack outcomes:
- Confidential information exposed outside approved scopes;
- Data exfiltration through model output or integrations;
- Regulatory and customer trust damage.
Unsafe AI actions occur when AI agents or autonomous agents can execute workflows beyond approved business logic or permissions. Andersen tests excessive agency, approval controls, and manipulated prompts to show which actions an attacker could trigger.
This often creates:
- Unauthorized automation across workflows;
- Business logic abuse and false actions;
- Unsafe AI behavior under manipulated prompts.
Insecure tool use arises when an AI application can call APIs, access files, or trigger downstream actions with more permission than intended. Andersen verifies read-only access boundaries, authorization checks, and tool-call validation before those privileges are abused.
The exposure usually looks like:
- Excessive agency and broad tool permissions;
- Action execution without sufficient validation;
- Exposure through third-party provider access.
RAG data poisoning occurs when an attacker alters indexed content or retrieval inputs to change a model response, expose hidden data sources, or steer a decision. Andersen tests model and data poisoning paths from ingestion through generated output.
Typical downstream effects:
- Corrupted retrieval results;
- Model output drift caused by poisoned sources;
- Hidden vectors that increase attack surface.
Authentication and authorization flaws let attackers use weak identity checks or over-privileged accounts to cross role and data boundaries. Andersen tests broken access controls and privilege escalation across AI applications, APIs, and connected services.
This can quickly turn into:
- Privilege escalation and account takeover;
- Cross-user data access and broken role boundaries;
- Business impact from unauthorized system actions.
Insecure AI APIs and integrations can accept untrusted input, expose credentials, or inherit excessive permissions from third-party services. Andersen evaluates validation, least-privilege controls, failure isolation, and supply chain risk across external models and products.
Common integration weaknesses:
- Unvalidated API responses from AI providers;
- Excessive permissions in third-party integrations;
- Supply chain risk from external AI models and services.
Privilege escalation occurs when an AI workflow bypasses role-based controls or mishandles a token to obtain greater access. Andersen validates vertical escalation to administrative functions and lateral movement across service boundaries with controlled exploits.
This can expose:
- Unauthorized access to admin functions and sensitive data;
- Lateral movement across systems and user contexts;
- Persistent compromise through elevated permissions.
AI model and training data security covers model weights, datasets, and fine-tuning pipelines targeted for theft, extraction, or poisoning. Andersen assesses inference security, dataset access controls, and supply chain risks that can compromise these assets.
Typical exposures include:
- Model theft and unauthorized model usage;
- Training data extraction through inference queries;
- Model poisoning and gradual behavioral manipulation.
Meet our expert
Anatolii Katiushin
Head of Offensive & Application Security
13+
years of experience
200+
completed engagements
2 in 3
critical flaws revealed
Anatolii leads our security services, turning complexity into business clarity, teamwork, and measurable maturity growth.
- Quantifies asset loss scenarios to prioritize threat models;
- Provides full-cycle protection: audit, simulate, remediate;
- Guides engagement roadmaps and drives security growth.
What you get from an AI penetration test
Andersen structures each engagement around evidence, business impact, and a remediation path your team can execute with confidence.
How we run an AI pentest
Andersen combines AI security tooling with manual validation: tooling gives machine-speed coverage, engineers confirm what is real. Every finding is validated before it reaches your team, so you act on real risk instead of scanner noise. Unlike point-in-time testing alone, continuous coverage keeps pace with releases.
We define the AI attack surface, trust boundaries, systems in scope, and the threats that matter most to the business.
- Model, tool, prompt, and data inventory;
- Risk scoring and testing boundaries;
- Clear success criteria for the engagement.
Why choose Andersen for AI penetration testing
Certified ethical hackers and security engineers
Our OSCP-, CEH-, and CISSP-certified ethical hackers validate attack chains through controlled exploitation and clear evidence. Backed by 19+ years in cybersecurity and 300+ security projects, they deliver findings teams can prioritize.
Full-cycle AI engineering expertise
Our AI engineers and data science specialists understand models, prompts, application architecture, and data pipelines. This delivery context lets our red teaming mirror how attackers actually target AI systems in production.
AI-Assisted security testing and engineering
We combine AI security tooling with human pentesters to validate vulnerabilities at scale. This approach evaluates LLM security, models, and integrations while reducing false positives and making security outcomes measurable.
Compliance-ready reporting and remediation support
Our reports map each vulnerability to severity, business impact, and security outcomes. We validate fixes under real-world conditions and provide ISO/IEC 27001- and AICPA SOC 2-aligned evidence for audit and remediation tracking.
Flexible engagement and delivery models
We offer fixed-scope pentests, PTaaS-style subscriptions, and retesting after remediation. Each model aligns scope with release cadence, risk tolerance, and delivery needs, giving teams predictable security coverage across planned releases.
What our clients say
Client feedback on our security work shows how our AI pen testing helps teams reduce exposure, validate fixes, and move with more confidence.
Get a risk-based AI pentesting roadmap and cost estimate today
AI security insights
Andersen provides AI pentesting for products, platforms, and teams that depend on models, agents, and AI-driven features. Our structured AI security testing process combines security tooling with human expertise to identify vulnerabilities and give your team a clear path to closing them.
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FAQ
AI pentesting evaluates large language models, prompts, APIs, agents, and data flows, while traditional pentesting focuses mainly on infrastructure, networks, and conventional applications. Both approaches are complementary: classic testing protects the surrounding environment, and AI testing examines model behavior, data exposure, integrations, and AI-specific attack surfaces.
Discuss your AI security risks before they reach production
What happens next?
We review your AI app, agent, or model stack and map the critical security risk areas;
Our team outlines the scope, likely attack paths, and the testing approach that fits your environment and the assets in scope;
You receive a clear proposal with timelines, coverage, and remediation priorities.
Customers who trust us
