prompt-injection-test

Test prompt injection vulnerabilities by applying a structured taxonomy that enumerates attack intents, payload techniques, and evasion layers to systematically identify weaknesses in LLM-integrated applications. This procedure validates guardrails and detects security flaws during red-team assessments.

The Arcanum PI Taxonomy is a structured framework for prompt injection testing that categorizes 13 attack intents, 18 payload techniques, and 20 evasions across attacker-controlled prompts, inputs, and surfaces to stress guardrails and model behavior under adverse conditions.

Validate LLM guardrails by applying payload techniques and evasion layers against the target model to stress data handling and model behavior, then documenting the results with severity levels, surface paths, and remediation guidance mapped to compliance references.

Yes, prompt injection testing applies to security auditing of chat interfaces, tooling, and MCP workflows by enumerating attack surfaces and intents to identify vulnerabilities across these LLM-integrated application environments.

A prompt injection findings report includes actionable severity ratings, surface paths, payload examples, and remediation guidance mapped to OpenCRE-like compliance references, supporting enterprise security and LLM01 deep-dive assessments.

Prompt injection testing covers 13 attack intents across attacker-controlled prompts and surfaces, applying 18 payload techniques and 20 evasions to comprehensively stress guardrails and detect model behavior weaknesses.