One managed stack. Full coverage.
Robusto, our security infra, gives teams red-teaming, hardening, and audit-grade reporting across classical ML and frontier LLM/agent systems. No stitching together point tools. No compliance gaps.
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Six integrated capabilities, from red-teaming to compliance. Covering every model type your team ships into production.
We attack your models on your behalf so you are not blindsided in production. Coverage spans image, tabular, graph, sensor/signal, LiDAR point cloud, audio, and LLM/agent systems. Output: a vulnerability map showing where the model fails, how, and under what threat conditions.
Drop-in hardened checkpoints (.pt, .onnx) that resist the attacks we found, with measured trade-offs on clean accuracy. Built for teams shipping into safety-critical or regulated environments.
Industry-standard jailbreak coverage (HarmBench, JailbreakBench, PAIR, TAP, AutoDAN), multi-modal red-teaming, and tool-use attack evaluation for agentic deployments. Hardening applies LoRA-based safety fine-tuning and owned-weights guardrails.
Labelled synthetic deepfake audio for voice-security vendors and anti-spoof model developers, covering vocoder artefacts, neural-coder imprints, TTS over-smoothing, and voice-clone surfaces. Closes the open-set generalisation gap left by ASVspoof-only training.
Constraint-respecting generative adversarial samples for fintech and regulated agentic environments: AML, KYC, credit scoring, and other tabular models. Failure points stay inside your feature constraints and data manifold, so attacks are realistic, not artefacts.
Signed attestations mapped to NIST AI RMF, EU AI Act, and, for financial-services customers, FinCEN/BSA, AMLD6, and BNM RMIT. Audit-ready, regulator-ready, customer-ready.
The broadest attack surface in the industry, from classical tabular models to frontier multimodal systems.
Test fraud and credit models against realistic constraints (age >= 18, income >= 0) so adversarial samples stay plausible for AML, KYC, and credit scoring.
Run a pre-deployment robustness audit and deliver hardened checkpoints for image classification, surveillance detection, and autonomous vehicle perception models.
Test log and anomaly classifiers against homoglyph and word-perturbation attacks, plus LLM data poisoning detection, audio deepfake classification, and guard-railing.
Harden recommender systems, fraud graphs, and anomaly detection networks against graph-structure and node-feature attacks.
Check time-series models for distribution shift in IoT deployments, and harden 3D point cloud perception pipelines for LiDAR-based systems.
Prepare pre- and post-hardening reports for NIST AI RMF, ISO 42001, and EU AI Act submissions.
Our roadmap pushes the frontier of AI security. Two critical defence layers are actively in development and coming soon to the Robusto platform.
Protecting your proprietary models from being stolen or replicated through query-based extraction and knowledge distillation attacks. Detects and mitigates model theft at the API boundary before your IP walks out the door.
A dedicated defence layer targeting direct and indirect prompt injection attacks against LLM-powered applications and agentic systems. Prevents adversarial instructions from hijacking model behaviour in production deployments.
These capabilities extend Robusto's coverage to the full adversarial lifecycle, from data ingestion through model deployment to live inference.
Tell us about your models and regulatory deadlines. We will respond with a scoped engagement and timeline.