Prompt injection
Direct manipulation of model instructions to override intended behavior, extract system prompts, bypass application-level restrictions, or reach functionality the user should not have.
AI Security
Security Testing for AI-Enabled Applications
AI security requires evaluating more than model output. We test the complete AI-enabled application, its data sources, its tool integrations, and the trust boundaries between them.
Why This Matters Now
Attacks on AI systems are being reported, not theorized
Our threat-intelligence platform tracks MITRE ATLAS, the framework covering attacks against AI systems that ATT&CK Enterprise does not describe. Over the last 365 days it has catalogued 565 article mentions across 71 distinct ATLAS techniques.
- ATLAS techniques
- 71observed in reporting
- Article mentions
- 565last 365 days
Aggregated from public security reporting, as of September 18, 2026.
The Right Question
Test the system, not the chatbot
Most published AI testing focuses on whether a model can be persuaded to produce undesirable text. That is a content-safety question. It is rarely the question that determines whether an AI feature is safe to ship.
The security question is what an attacker can accomplish through the AI-enabled system: which data it can reach, which actions it can take on a user's behalf, which downstream systems trust its output, and whether the authorization decisions live in code or in a system prompt.
The core principle. AI security testing should determine what an attacker can accomplish through the AI-enabled system, not simply whether the model can be convinced to generate undesirable responses.
Trust boundaries in a typical AI-enabled application
Coverage
What AI security testing covers
Scope depends on your architecture. An internal assistant with read-only retrieval needs different coverage than an agent that can act on production systems.
Indirect prompt injection
Instructions delivered through content the model consumes rather than through the user: retrieved documents, web pages, emails, tickets, file contents, or the output of another system. This is where the highest-impact findings usually live.
RAG and data-source security
Whether retrieval respects per-user access controls, whether the index contains content the requesting user is not entitled to see, and whether documents can be poisoned to influence downstream behavior.
Sensitive data leakage
Exposure of system prompts, credentials, keys, internal configuration, other users' data, or training and context data through model responses, error messages, or logging.
Authorization bypass
Whether authorization is enforced in application code or delegated to model instructions, and whether the model can be induced to act with privileges belonging to a different user or role.
Tool and function invocation
Which tools the model can call, with what parameters, under what validation, and whether an attacker can drive a chain of tool calls toward an action the user never requested.
Plugin and integration security
Trust relationships between the AI system and connected services, including credential scope, token handling, and whether integrations inherit more access than the feature requires.
Model API security
Authentication and authorization on model endpoints, key management, tenant isolation, rate limiting and cost abuse, and exposure of inference infrastructure.
Agentic workflows
Multi-step and multi-agent systems where output from one step becomes the input to the next, including whether a single injected instruction can persist across steps or escalate as the workflow proceeds.
Cross-system attack paths
How an AI-mediated foothold connects to the rest of the environment: the identity the system runs as, the network it sits in, and the systems that trust it.
Method
Adversarial testing, grounded in the architecture
Testing begins with understanding how the system is actually assembled, because that is what determines which attacks are worth attempting.
- Map the architecture. Data sources, tools, identities, trust boundaries, and where each piece of context originates.
- Identify the untrusted inputs. Everything the model consumes that an attacker can influence, directly or indirectly.
- Define the objectives. The specific outcomes worth pursuing: data access, privileged action, lateral reach.
- Test adversarially. Manual, iterative attempts against those objectives, chained across the full system.
- Report with impact. Demonstrated consequence, architectural root cause, and remediation that survives a model swap.
Who This Is For
When AI security testing is worth doing
AI testing is most valuable at the point where an AI feature gains access to real data or the ability to take real actions. Before that, the risk is largely reputational; after it, the risk is the same class of risk as any other application with production access.
- SaaS products shipping an LLM-backed feature
- Internal assistants connected to company data
- Customer-facing support and sales agents
- RAG systems indexing sensitive documents
- Agentic workflows with write access to systems
- AI features subject to customer security review
- Teams that need an independent assessment before launch
Discuss an AI security assessment
Whether you are shipping an LLM feature, deploying an internal assistant, or building agentic workflows, we can help you determine what needs to be tested and how deeply.