Each OWASP LLM failure mode maps to patterns that exist in the EAAPL catalog. Use the list as a threat-model reference, then test whether the defensive boundary exists in the actual system.
Adversarial inputs override trusted instructions or manipulate retrieved context, allowing an attacker to redirect model behaviour and downstream actions.
Instruction integrity loss, data exfiltration and unauthorised agent actions.
Model output reaches browsers, shells, APIs or databases without a validation boundary, turning generated text into an execution surface.
Code injection, XSS, SSRF, command execution and unsafe downstream mutations.
Compromised training or fine-tuning data silently changes model behaviour, embeds backdoors or creates systematic bias before runtime controls can intervene.
Persistent model degradation, hidden backdoors and weak data provenance.
Long contexts, recursive generation and unbounded tool loops exhaust inference capacity or create disproportionate cost and latency.
Availability failure, cost spikes and cascading retry pressure.
Third-party model weights, datasets, plugins, SDKs and providers introduce unverified dependencies into the AI trust boundary.
Untraceable model risk, hidden compromise and audit failure.
Models reveal PII, credentials, proprietary context or retrieved sensitive information through memorisation, prompt manipulation or weak data controls.
Privacy breach, credential exposure and confidential-data leakage.
Connected tools lack strong authentication, input validation or least privilege, allowing model-driven calls to exceed the intended authority.
Privilege escalation, lateral movement and unintended writes.
An autonomous system receives more permission or irreversible capability than the task requires without an adequate approval boundary.
High-impact autonomous actions, unsafe production mutations and accountability failure.
Users or automated workflows accept model output without independent verification, especially where fluent answers hide uncertainty or hallucination.
Flawed regulated decisions, liability exposure and loss of human oversight.
Repeated querying or weakly protected artefacts expose proprietary weights, prompts, parameters or fine-tuned behaviour.
IP theft, competitive exposure and circumvention of safety controls.
Use Architecture Intelligence to trace the actual attack path, missing control and evidence state for the system under review.
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