MLDR
HiddenLayer's MLDR (Machine Learning Detection and Response) is an enterprise AI security platform purpose-built to protect ML models throughout their lifecycle—from development and supply chain to production inference.
Publisher review
HiddenLayer's MLDR (Machine Learning Detection and Response) is an enterprise AI security platform purpose-built to protect ML models throughout their lifecycle—from development and supply chain to production inference. Founded in 2022 by Chris Sestito, Tanner Burns, and James Ballard (former Cylance threat researchers), the company was motivated by a real-world inference attack that bypassed Cylance's Windows executable AI model. MLDR is designed for security teams and ML engineers at large organizations that need non-invasive visibility into model risks without accessing weights, training data, or prompts. The platform has raised $50M in Series A funding led by M12 (Microsoft’s Venture Fund) and Moore Strategic Ventures, and has disclosed 48+ CVEs in ML frameworks while holding 25+ granted patents in adversarial detection and model protection.
MLDR operates as a software-based, non-invasive layer that monitors model inputs and outputs in real time to detect and respond to security threats. Its ModelScanner analyzes 35+ model formats for supply chain threats including backdoors, trojans, and serialization exploits. The AI Runtime Security module provides real-time defense against adversarial attacks, prompt injection, and model evasion in production. The platform also covers supply chain scanning, runtime defense, and adversarial red teaming across the full model lifecycle. It generates AI Bill of Materials (AIBOM) and includes agentic AI protection, enabling teams to understand model composition and detect anomalies without modifying the model itself.
In the AI security market, MLDR competes directly with Palo Alto Networks and Prediction Guard. Unlike traditional cybersecurity tools designed for code and infrastructure, MLDR is purpose-built for ML model artifacts and inference behaviors. Its non-invasive approach differentiates it from solutions that require model access or retraining. The company's deep expertise—evidenced by 48+ disclosed CVEs and 25+ patents—positions it as a specialized player in adversarial ML defense, though it lacks the broader security platform integration of larger competitors like Palo Alto Networks.
The primary trade-off with MLDR is its complexity: setup requires significant technical expertise and engineering effort, making it less accessible for smaller teams. Pricing is customized per enterprise, with costs determined by deployment scale and feature requirements—potentially prohibitive for smaller enterprises. The platform's focus on ML-specific threats means it does not replace general cybersecurity tools, requiring integration with existing security stacks. Additionally, while it provides strong visibility into model risks, it does not offer built-in model governance or compliance features beyond security monitoring.
How it works
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Real-Time Monitoring
Continuously monitors AI model inputs and outputs to detect and respond to security threats as they occur in production.
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Proactive Security Scanning
Regularly scans AI models to identify vulnerabilities before attackers exploit them, analyzing 35+ model formats.
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Automated Reporting
Generates comprehensive reports on the security status of AI systems, including AI Bill of Materials (AIBOM).
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Attack Vector Protection
Shields AI systems from reverse engineering, adversarial inputs, data poisoning, prompt injection, and inference manipulation.
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Machine Learning Detection and Response
Non-invasive software-based approach that monitors algorithm inputs and outputs without accessing weights or training data.
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Supply Chain Scanning
Analyzes model artifacts for backdoors, trojans, and serialization exploits across 35+ model formats.
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Adversarial Red Teaming
Simulates attacks on models to test defenses, covering the full lifecycle from training to production deployment.
Strengths and trade-offs
Strengths
- Provides strong visibility into AI and ML model risks without requiring access to weights, training data, or prompts.
- Easy to set up for teams with ML expertise, with a non-invasive software-based approach that avoids model modification.
- Real-time threat detection against adversarial attacks, prompt injection, and inference manipulation in production environments.
- Protects against multiple attack vectors including reverse engineering, adversarial inputs, and data poisoning, backed by 25+ granted patents.
Trade-offs
- Complex setup requiring significant technical expertise and engineering effort, limiting accessibility for smaller teams.
- High cost for smaller enterprises, with pricing customized per deployment scale and feature requirements.
- Demands significant engineering resources for deployment and ongoing maintenance, which may strain lean security teams.
- Focuses exclusively on ML-specific threats, requiring integration with broader cybersecurity tools for full coverage.
Pricing context
Customized solutions tailored to specific enterprise needs, with costs determined by variables such as deployment scale and feature requirements; no public tier pricing available.
Getting started with MLDR
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Request enterprise access
Contact HiddenLayer's sales team through their website to request a demo or trial. Provide details about your deployment scale and security requirements to receive a customized onboarding plan and pricing quote.
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Integrate with ML infrastructure
Connect MLDR to your model serving infrastructure by deploying its non-invasive monitoring agent. Configure the agent to intercept model inputs and outputs without accessing weights or training data, following the provided integration guide.
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Configure supply chain scanning
Set up ModelScanner to analyze your model artifacts across 35+ formats. Define scanning policies to detect backdoors, trojans, and serialization exploits, and schedule regular scans for new models entering your pipeline.
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Enable runtime threat detection
Activate the AI Runtime Security module to monitor production inference traffic. Configure alert thresholds for adversarial attacks, prompt injection, and model evasion, and integrate with your existing SIEM for centralized logging.
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Review AIBOM reports
Generate an AI Bill of Materials report to inventory model components and dependencies. Use this report to assess supply chain risks, share with compliance teams, and track changes across model versions over time.
Frequently Asked Questions
What is MLDR and how does it protect AI models?
MLDR, or Machine Learning Detection and Response, is an enterprise AI security platform from HiddenLayer that monitors model inputs and outputs in real time. It detects threats like adversarial attacks and prompt injection without accessing weights, training data, or prompts.
How does MLDR's non-invasive monitoring work?
MLDR operates as a software-based layer that analyzes algorithm inputs and outputs without modifying the model or accessing its internal data. This provides visibility into risks while preserving model integrity, making it suitable for production environments where model changes are undesirable.
What types of threats does MLDR defend against?
MLDR protects against adversarial inputs, data poisoning, prompt injection, reverse engineering, and inference manipulation. It also scans for supply chain threats like backdoors and trojans across 35+ model formats, backed by 25+ granted patents in adversarial detection.
How does MLDR compare to Palo Alto Networks for AI security?
MLDR is purpose-built for ML model artifacts and inference behaviors, unlike traditional tools focused on code and infrastructure. It offers specialized adversarial defense with 48+ disclosed CVEs, but lacks the broader security platform integration of larger competitors like Palo Alto Networks.
What is the pricing and setup complexity for MLDR?
MLDR pricing is customized per enterprise based on deployment scale and feature requirements, with no public tier pricing available. Setup requires significant technical expertise and engineering effort, making it less accessible for smaller teams and potentially costly for smaller enterprises.
What features does MLDR offer for supply chain security?
MLDR's ModelScanner analyzes 35+ model formats for supply chain threats such as backdoors, trojans, and serialization exploits. It generates an AI Bill of Materials (AIBOM) to document model composition, helping teams understand risks without modifying the model itself.
Alternatives
How MLDR compares
Direct head-to-head against 2 competitors. Picked by 7wData.
MLDR
- Pricing
- Customized solutions tailored to specific enterprise needs, with costs determined by variables such as deployment scale and feature requirements; no public tier pricing available.
- Target
- HiddenLayer's MLDR (Machine Learning Detection and Response) is an enterprise AI security platform purpose-built to protect ML models throughout their lifecycle—from development and supply chain
- Strength
- Provides strong visibility into AI and ML model risks without requiring access to weights, training data, or prompts.
- Watch for
- Complex setup requiring significant technical expertise and engineering effort, limiting accessibility for smaller teams.
PageCrawl
- Pricing
- Free (6 monitors), $8/mo
- Target
- E-commerce and SaaS price monitoring
- Deployment
- Cloud SaaS
- Strength
- Smart price detection with browser rendering for JS-heavy sites
- Watch for
- Limited to 6 monitors on free tier; no native CRM integration
Nimble
- Pricing
- Custom/Contact sales
- Target
- Enterprise pricing intelligence and web data extraction
- Deployment
- Cloud SaaS + SDK
- Strength
- Web Search Agents for real-time structured data extraction
- Watch for
- Enterprise pricing; complex setup for non-developer teams
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Sources
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