Artificial intelligence is increasingly being embedded into customer support bots, internal knowledge bases, document summarization tools, code assistants, enterprise search, decision-support workflows, autonomous agents, Retrieval-Augmented Generation pipelines, and integrations with APIs, databases, email, files, and business systems.

These systems create a different type of attack surface. Traditional application testing is still important, but AI and LLM applications also introduce risks such as prompt injection, sensitive information disclosure, system prompt leakage, excessive agency, unsafe tool use, vector and embedding weaknesses, model and data poisoning, misinformation, improper output handling, and unbounded consumption.

Cryptika provides Artificial Intelligence Penetration Testing to help organizations identify, validate, prioritize, and remediate security weaknesses in AI-enabled systems before they create business, privacy, compliance, operational, or reputational impact.

What Artificial Intelligence Penetration Testing Is

Artificial Intelligence Penetration Testing is an authorized security assessment of AI-enabled applications, LLM systems, RAG pipelines, AI agents, chatbots, code assistants, AI APIs, and AI-integrated business workflows.

The service focuses on how the AI system behaves under adversarial, unintended, manipulated, or high-risk input conditions. It also reviews how the AI application interacts with users, data sources, tools, plugins, APIs, vector databases, backend systems, and downstream components.

The objective is not only to test whether the model responds safely. The objective is to understand whether the full AI application can be misused to disclose sensitive information, bypass controls, manipulate outputs, abuse tools, trigger unsafe actions, expose internal logic, consume excessive resources, or influence business decisions incorrectly.

Why Organizations Need This Service

AI systems are often deployed quickly to improve productivity, automate support, summarize documents, assist developers, answer customer questions, or connect users with internal knowledge. In many cases, the security review focuses on the web application, cloud configuration, or API layer, while the AI-specific risks remain untested.

This creates hidden exposure. A chatbot connected to internal documents may leak sensitive information. A RAG system may retrieve content across the wrong user context. An AI agent with tool access may perform actions beyond its intended role. A system prompt may expose internal rules or sensitive architecture details. A generated output may be passed downstream without proper validation and create application-layer impact.

Artificial Intelligence Penetration Testing helps organizations understand these risks in a controlled way, with evidence-based findings and remediation guidance that technical, security, risk, compliance, and management teams can act on.

Typical Buyer Situations

Organizations usually request this service when they are:

• Launching an AI chatbot, assistant, RAG system, or AI-enabled platform.
• Integrating LLMs with business applications, APIs, databases, files, email, calendars, or workflow tools.
• Using AI for customer support, internal knowledge search, document summarization, coding support, HR, finance, healthcare, legal, or operational use cases.
• Preparing AI systems for security review, customer assurance, internal audit, or compliance assessment.
• Concerned about prompt injection, jailbreaking, system prompt leakage, sensitive data disclosure, or unsafe output handling.
• Reviewing AI systems before production release.
• Assessing the security of third-party AI tools or AI-enabled SaaS platforms.
• Aligning AI governance with ISO/IEC 42001, ISO/IEC 27001, ISO/IEC 27701, NIST CSF 2.0, privacy laws, or internal AI policies.

Cryptika’s methodology follows a structured, evidence-based process designed to balance technical depth, safety, repeatability, and business relevance.

1. Preparation: AI Ecosystem Scoping and Asset Inventory

Cryptika starts by understanding the AI system boundary, deployment architecture, model provider, middleware, vector database, user interfaces, APIs, tool/function-calling schema, data sources, and trust assumptions.

This phase identifies direct and indirect input channels, sensitive data categories, external actions the AI system can influence, and privilege boundaries that may affect testing. The purpose is to avoid testing the model in isolation while missing the real application, data, and workflow risk.

2. Mapping: Attack Surface Enumeration and Capability Discovery

Cryptika maps what the AI system can access, reveal, invoke, retrieve, influence, or decide. This includes hidden or undocumented capabilities, tool access, system behavior, prompt boundaries, role assumptions, RAG data paths, and user-context handling.

This phase helps identify where prompt manipulation, system prompt leakage, excessive agency, or tool misuse may become realistic attack paths.

3. Probing: Controlled Active Exploitation and Chaining

Cryptika performs controlled testing to validate discovered attack vectors. This may include direct and indirect prompt injection testing, RAG manipulation attempts, system prompt leakage checks, sensitive information extraction tests, tool/function abuse testing, output-handling validation, and chained scenarios where one weakness increases the impact of another.

Testing is performed within the approved rules of engagement and avoids uncontrolled harmful actions. The purpose is to prove risk safely, document evidence, and determine business impact.

4. Risk Assessment: Business-Aligned Prioritization

Validated findings are assessed based on likelihood, impact, affected data, affected users, affected systems, blast radius, exploitability, detectability, existing mitigations, and business context.

This translates AI security findings into practical executive and technical language so the organization can understand which issues require immediate remediation and which require longer-term control improvement.

5. Mitigation and Hardening Roadmap

Cryptika provides prioritized remediation guidance. This may include improving authorization outside the model layer, reducing tool permissions, adding human confirmation for high-impact actions, improving input and output validation, reducing sensitive data exposure, separating user contexts, strengthening RAG access control, enhancing logging and monitoring, improving guardrails, and planning regular retesting.

Methodology Basis

This service is based on AI and application-security testing practices, including:

• OWASP Top 10 for Large Language Model Applications.
• OWASP Application Security Verification Standard, where application controls are in scope.
• OWASP Web Security Testing Guide, where web interfaces and APIs are in scope.
• ISO/IEC 42001 AI management system governance themes.
• ISO/IEC 27001 information security management controls.
• ISO/IEC 27701 privacy management themes where personal data is involved.
• NIST Cybersecurity Framework 2.0 governance, protection, detection, response, and recovery themes.
• Client-specific AI governance, security, privacy, and compliance requirements.

The methodology is adjusted based on the AI system type, business use case, deployment model, data sensitivity, agent capability, tool access, regulatory exposure, and agreed rules of engagement.

What the Client Should Prepare

The client should prepare:

• AI system description and business use case.
• Architecture, workflows, APIs, integrations, and data flows.
• Models, providers, tools, plugins, vector databases, and RAG sources.
• User roles, permissions, test accounts, and approved test environment.
• System prompts, guardrails, monitoring access, and AI policies where shareable.
• Clear approval for allowed and prohibited testing actions.

What Cryptika Tests and Reviews

Depending on the agreed scope, Cryptika may assess:

• AI application architecture and trust boundaries.
• User input channels and indirect input sources.
• Prompt injection and jailbreak resistance.
• System prompt leakage risk.
• Sensitive information disclosure.
• RAG pipeline and vector database access controls.
• Embedding and retrieval weaknesses.
• Tool, plugin, function-calling, and agent permissions.
• Excessive agency and unsafe autonomous actions.
• Improper output handling before downstream use.
• Data and model poisoning exposure.
• Supply chain risks involving models, datasets, dependencies, plugins, and AI providers.
• API and backend integration security.
• Identity, authorization, and user-context enforcement.
• Abuse cases, rate limits, resource consumption, and denial-of-wallet risks.
• Logging, monitoring, alerting, and audit trail readiness.
• Human confirmation gates for high-impact actions.
• Data minimization and privacy controls.
• Business impact of validated AI security weaknesses.

Key Activities

Artificial Intelligence Penetration Testing may include:

• AI architecture, workflow, API, RAG, tool, and data-flow review.
• Prompt injection, jailbreak, and system prompt leakage testing.
• Sensitive data disclosure and RAG access-control testing.
• Tool/function abuse, excessive agency, and output-handling review.
• Supply chain, model dependency, poisoning, logging, and monitoring review.
• Risk rating, business impact analysis, remediation guidance, and retesting where agreed.