Prompt-injection testing
Direct injection (OWASP LLM01), indirect injection via RAG corpus, tool-result injection. Probe families align to OWASP LLM Top 10 (2025).
LLM Security Posture Management is a focused layer within AI Security Posture Management (AI-SPM) that helps teams test, monitor, and govern LLM applications, prompt flows, model endpoints, and runtime controls.
AI-SPM covers the full AI control surface (assets, agents, MCP servers, RAG, vector DBs, gateways). LLM Security focuses the lens on the language-model-specific surface: prompts, completions, model endpoints, and inline filtering.
Direct injection (OWASP LLM01), indirect injection via RAG corpus, tool-result injection. Probe families align to OWASP LLM Top 10 (2025).
Improper output handling (LLM05). Detects responses that leak credentials, internal URLs, or executable content; flags downstream consumers without sanitisation.
Probes for inadvertent leakage of training data, system prompts, embedded credentials, or context-window contents (LLM02, and LLM07 for system prompts). 217 DLP patterns + custom regex.
Tests for over-broad tool authorisations and missing human-in-the-loop on destructive operations (LLM06 + OWASP Agentic Top 10).
Optional self-hosted DLP firewall + tool allowlist + per-domain budgets. Prompts never leave the customer VPC when the gateway is in use.
Each adversarial response is scored first by deterministic checks that run inside our own infrastructure; a response they cannot settle goes to a single model judge, and low-confidence results are reported as inconclusive rather than counted as a pass. A three-judge plus meta-judge panel is built and switched off.
LLM01 Prompt injection
LLM02 Sensitive information disclosure
LLM03 Supply chain
LLM04 Data and model poisoning
LLM05 Improper output handling
LLM06 Excessive agency
LLM07 System prompt leakage
LLM08 Vector and embedding weaknesses
LLM09 Misinformation
LLM10 Unbounded consumption
Auditors verify our control mapping against the same documents we read. Each item below points to the canonical publication.
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Most teams start with LLM Security testing and widen the scope to AI-SPM as agents, MCP servers, RAG pipelines, and runtime gateways come into production.