Context Discovery

Context discovery is the process of learning what a chatbot is for, what it can access, and where its refusal boundaries appear to be. In AI security testing, this early reconnaissance phase often determines whether later prompt attacks can be tailored effectively.

Expanded Definition

Context discovery is the reconnaissance phase of AI security testing in which an attacker or tester infers an assistant’s purpose, tool access, instruction hierarchy, and refusal patterns. It sits upstream of prompt injection, jailbreak attempts, and tool abuse because it helps shape the next query with enough specificity to bypass weak safeguards. In practice, the term is used most often in chatbot and agentic AI assessments where the model’s behaviour is not fully exposed through documentation alone. Guidance in the field is still evolving, so teams often borrow structure from broader governance models such as the NIST Cybersecurity Framework 2.0 while adapting it to conversational systems.

Context discovery is not the same as ordinary functional testing. Functional testing asks whether the system answers correctly; context discovery asks what hidden assumptions, routing rules, and operational boundaries can be inferred from the interaction surface. That distinction matters when an assistant can call APIs, retrieve internal documents, or trigger workflows through connected tools. The most common misapplication is treating context discovery as harmless curiosity, which occurs when security teams ignore early probing that reveals model scope, available tools, and refusal edge cases.

Examples and Use Cases

Implementing context discovery rigorously often introduces investigation overhead, requiring organisations to weigh faster testing against the need to avoid exposing sensitive instructions or connected resources.

• A tester asks a support bot about billing, HR, and engineering topics to determine which domain it is actually authorised to discuss, then uses that boundary map to craft a narrower prompt injection.
• An assessor probes whether the assistant can summarise internal files, call external endpoints, or execute actions through an agent tool chain, revealing access paths before any exploit attempt.
• A red teamer intentionally varies tone, format, and role-play to infer which refusals are policy-based and which are triggered by simple keyword filters, a pattern often discussed in OWASP guidance for LLM applications.
• A security engineer checks whether a customer-facing chatbot leaks system prompts, tool names, or hidden routing instructions when asked innocuous questions about “how you work.”
• An incident responder reviews chat logs after suspicious probing to identify whether the actor mapped available context before attempting data extraction or action hijacking.

Why It Matters for Security Teams

Context discovery matters because many AI incidents begin with an attacker learning just enough about the system to tailor the real attack. If the assistant’s scope, retrieval sources, and tool permissions are discoverable, then prompt attacks become more precise and more likely to succeed. That is especially important for agentic AI, where hidden context can include execution authority, workflow triggers, and delegated access to secrets, tickets, or internal systems. Security teams should treat context discovery as an early warning signal rather than a nuisance, because it often indicates that the model is leaking structural information that should remain opaque. The issue also intersects with governance: a well-defined AI security program should inventory what the system can access, what it should refuse, and how those boundaries are validated over time, consistent with NIST Cybersecurity Framework 2.0 and related AI risk practices.

Organisations typically encounter the real impact only after a probing conversation is followed by targeted prompt injection or tool misuse, at which point context discovery becomes operationally unavoidable to investigate and contain.

Related resources from NHI Mgmt Group

• What breaks when NHI discovery does not include business context?
• Why do discovery tools fail when permissions context is missing?
• Why do AI-driven vulnerability discovery tools need good asset context?
• Why does context matter more than asset discovery in exposure management?