HiddenLayer AISec Platform

Detects and throttles adversarial query patterns designed to reconstruct model weights or replicate model behavior through repeated inference.

Embeds imperceptible markers in model outputs enabling detection of unauthorized model copying, redistribution, or derivative deployment.

Applies rate limiting, anomaly detection, and abuse pattern blocking to model inference endpoints, distinct from general API security.

Evaluates model behavior against adversarial input perturbations (FGSM, PGD, CW attacks) to quantify robustness before production deployment.

Defends against attacks that reconstruct training data or determine whether a record was in the training set, by detecting and limiting query patterns that probe the model for memorized data.

Automatically discovers AI models, LLM API connections, ML pipelines, and AI-enabled SaaS applications in use across the organization, including those deployed without IT authorization.

Maps data lineage and provenance across AI training and inference pipelines, tracing how PII, PHI, and IP move into models and external services.

Scores deployed AI models by risk level based on data sensitivity processed, deployment scope, capability classification, and applicable regulatory requirements.

Detects sensitive or regulated data in AI training, fine-tuning, or third-party LLM flows without appropriate controls, such as unencrypted PII in inputs or PHI sent to external APIs.

Discovers AI model and inference endpoints and flags public exposure, weak authentication, default credentials, or excessive permissions as posture misconfigurations.

Attacks AI agents through their tools, memory, and connected services using multi-step techniques such as tool misuse, goal hijacking, and indirect injection, surfacing exploit paths unique to autonomous agents.

Autonomously plans and executes multi-step adversarial campaigns against AI systems, emulating real attacker workflows across reconnaissance, exploitation, and escalation rather than running a fixed checklist of tests.

Reports validated AI vulnerabilities with reproduction evidence, attacker context, and remediation guidance, mapped to the OWASP LLM Top 10, MITRE ATLAS, EU AI Act, and NIST AI RMF for auditable AI risk reporting.

Tests LLMs and AI applications against a library of direct and indirect prompt-injection and jailbreak techniques, reporting which payloads bypass system instructions and safety controls.

Discovers AI assets, including shadow models, agents, and inference endpoints, and maps the reachable attack surface to scope and target red-team campaigns. Offensive reconnaissance, distinct from posture inventory.

Routes high-value automated findings to specialist AI red teamers for manual exploitation, chaining, and depth beyond automated coverage, blending platform testing with human expertise.

Generates adversarial inputs across text, image, and audio modalities to test model evasion and misclassification, extending red teaming beyond text-only prompt attacks.