NeuralTrust Model Scanner
Scans AI models for malicious code, vulnerabilities, and unsafe artifacts pre-deployment.
NeuralTrust Model Scanner
Scans AI models for malicious code, vulnerabilities, and unsafe artifacts pre-deployment.
Data verified Jun 2026
ADYour product here. Reach security decision-makers.Launch a campaignNeuralTrust Model Scanner Description
Mlsecops
Supply Chain Security
Scanner
LLM Security
Software Supply Chain
CI/CD
MITRE Attack
OWASP
Dependency Scanning
NeuralTrust Model Scanner is a security tool designed to inspect AI models and their associated artifacts before deployment, targeting vulnerabilities introduced through the AI supply chain. The scanner performs deep inspection across multiple layers of an AI system, including model weights, preprocessing scripts, serialized artifacts, and model-linked code. It identifies a range of vulnerability classes mapped to established frameworks including OWASP, MITRE, CWE, and AI-specific standards. Vulnerability categories detected include: - Deserialization vulnerabilities (CWE-502): unsafe pickle opcodes, unsafe deserialization patterns, and potential pickle attack patterns - Module import vulnerabilities (CWE-506): dangerous module references, unsafe imports, and module references in __reduce__ methods - Network vulnerabilities (CWE-924): suspicious network activity, external network requests, and URLs embedded in pickle files - Code execution vulnerabilities (CWE-94): dynamic code execution, generic code execution patterns, and embedded code objects - Model-specific issues (CWE-506, CWE-1294): suspicious state dictionary keys, tensors with NaN/Inf values, extreme tensor values, and suspicious value distributions - File integrity and corruption (CWE-1294): file corruption, invalid pickle formats, and pickle load errors - File system vulnerabilities (CWE-22): unauthorized file system access and unexpected file references - Data exfiltration vulnerabilities (CWE-200): detection of data exfiltration patterns The tool also performs artifact integrity verification using cryptographic and fuzzy hashes to detect drift across environments. It integrates with CI/CD pipelines and model repositories to automatically identify changes in models across providers.
NeuralTrust Model Scanner FAQ
Common questions about NeuralTrust Model Scanner including features, pricing, alternatives, and user reviews.
NeuralTrust Model Scanner is Scans AI models for malicious code, vulnerabilities, and unsafe artifacts pre-deployment, developed by NeuralTrust. It is a AI Security solution designed to help security teams with Mlsecops, Supply Chain Security, Scanner.
NeuralTrust Model Scanner offers the following core capabilities:
1.Detection of deserialization vulnerabilities (CWE-502) including unsafe pickle opcodes and attack patterns
2.Detection of dangerous module imports and references (CWE-506)
3.Detection of network vulnerabilities including embedded URLs and external requests (CWE-924)
4.Detection of dynamic and generic code execution vulnerabilities (CWE-94)
5.Model-specific inspection for suspicious tensor values, NaN/Inf tensors, and state dictionary anomalies
6.File integrity and corruption detection including invalid pickle formats (CWE-1294)
7.File system vulnerability detection including unauthorized access patterns (CWE-22)
8.Data exfiltration vulnerability detection (CWE-200)
9.Artifact integrity verification using cryptographic and fuzzy hashes
10.Framework mapping to OWASP, MITRE, CWE, and AI-specific standards for compliance support
NeuralTrust Model Scanner integrates natively with CI/CD pipelines. Integration support lets security teams connect NeuralTrust Model Scanner to existing SIEM, ticketing, identity, and notification systems without custom development.
NeuralTrust Model Scanner is deployed as a cloud solution, suited to startup, smb, mid-market, enterprise organizations looking to operationalize ai security. The commercial offering is positioned for production security operations with vendor support and SLAs.
NeuralTrust Model Scanner is built for security teams handling Mlsecops, Supply Chain Security, Scanner, LLM Security. It supports workflows including detection of deserialization vulnerabilities (cwe-502) including unsafe pickle opcodes and attack patterns, detection of dangerous module imports and references (cwe-506), detection of network vulnerabilities including embedded urls and external requests (cwe-924). Teams typically adopt NeuralTrust Model Scanner when they need to ai security capabilities integrated into their existing stack. Explore similar tools at https://cybersectools.com/alternatives/neuraltrust-model-scanner
NeuralTrust Model Scanner is a commercial AI Security solution. For detailed pricing information, visit https://neuraltrust.ai/model-scanner or contact NeuralTrust directly.
Popular alternatives to NeuralTrust Model Scanner include:
1.Protect AI Guardian - AI model security scanner detecting threats across 35+ model formats (Commercial)
2.Bosch AIShield AISpectra - API-based AI/ML vulnerability assessment and defense platform. (Commercial)
3.Duality Secure AI Collaboration Platform - Platform for privacy-protected AI/ML model training on sensitive data. (Commercial)
4.Enveil Secure AI - PETs-powered encrypted ML training, inference, and validation across data silos. (Commercial)
5.ObjectSecurity FortiLayer - AI/ML model security tool for internal vulnerability analysis in defense apps. (Commercial)
Compare all NeuralTrust Model Scanner alternatives at https://cybersectools.com/alternatives/neuraltrust-model-scanner
NeuralTrust Model Scanner is for security teams and organizations that need Mlsecops, Supply Chain Security, Scanner, LLM Security, Software Supply Chain. It's particularly suitable for enterprises requiring robust, commercial-grade security capabilities. Other AI Security tools can be found at https://cybersectools.com/categories/ai-security
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