{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_mlcode","slug":"mlcode","name":"MLCode","type":"product","url":"https://mlcode.io","page_url":"https://unfragile.ai/mlcode","categories":["automation","code-review-security"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_mlcode__cap_0","uri":"capability://automation.workflow.multi.environment.data.security.policy.orchestration","name":"multi-environment data security policy orchestration","description":"Centralizes and synchronizes data security policies across heterogeneous deployment environments (cloud, on-premises, hybrid) using HexaKube's distributed orchestration layer. The system maintains a single source of truth for security rules while translating them into environment-specific enforcement mechanisms, eliminating manual policy duplication and drift that occurs when teams manage separate security stacks per environment.","intents":["I need to enforce the same data access controls across AWS, GCP, and on-prem Kubernetes clusters without rewriting policies for each platform","I want to prevent policy drift when security rules change — ensure updates propagate consistently across all environments simultaneously","I need to audit which security policies are active in each environment and detect misconfigurations before they cause compliance violations"],"best_for":["Enterprise ML ops teams managing multi-cloud or hybrid infrastructure","Organizations with strict compliance requirements (HIPAA, SOC2, GDPR) across distributed environments","Data teams scaling from single-environment to multi-environment deployments"],"limitations":["Requires pre-existing infrastructure instrumentation — cannot enforce policies on unmonitored data pipelines","Policy translation overhead may introduce 100-500ms latency per environment sync depending on policy complexity","Limited to environments where HexaKube agents can be deployed; air-gapped systems require custom integration"],"requires":["Network connectivity between MLCode control plane and target environments","Deployment permissions in target cloud/on-prem infrastructure","Existing data pipeline infrastructure (Spark, Airflow, Kubernetes, or cloud-native services)"],"input_types":["security policy definitions (YAML/JSON)","environment topology/metadata","data lineage graphs"],"output_types":["environment-specific policy configurations","compliance audit reports","policy enforcement logs"],"categories":["automation-workflow","safety-moderation","infrastructure-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mlcode__cap_1","uri":"capability://data.processing.analysis.automated.data.lineage.tracking.for.ml.pipelines","name":"automated data lineage tracking for ml pipelines","description":"Automatically captures and maps data flow through ML training, inference, and batch processing pipelines by instrumenting data access points (data loaders, feature stores, model inputs/outputs). The system builds a directed acyclic graph (DAG) of data transformations and identifies which raw data sources feed into which models, enabling security policies to be applied at the source rather than reactively at the point of breach.","intents":["I need to know which raw data sources are used by each ML model so I can apply data masking policies at the source","I want to trace a data breach backward to identify all models and downstream systems that may have been affected","I need to demonstrate data lineage to auditors to prove compliance with data minimization and purpose limitation principles"],"best_for":["ML teams with complex feature engineering pipelines involving multiple data sources","Organizations subject to data residency or data minimization regulations","Teams building multi-stage ML systems (feature engineering → training → inference)"],"limitations":["Requires instrumentation of data access layers — custom data loaders or proprietary data systems may require manual integration","Lineage tracking adds computational overhead to data pipelines (estimated 5-15% depending on pipeline complexity)","Cannot retroactively reconstruct lineage for historical data; only tracks lineage from deployment forward"],"requires":["Access to data pipeline source code or ability to inject instrumentation","Supported data frameworks (Spark, Pandas, TensorFlow, PyTorch, or cloud-native services)","Persistent storage for lineage graph (included in MLCode platform)"],"input_types":["data pipeline code","model training/inference logs","feature store metadata"],"output_types":["data lineage DAG (JSON/GraphQL)","impact analysis reports","lineage visualization"],"categories":["data-processing-analysis","memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mlcode__cap_10","uri":"capability://automation.workflow.model.versioning.and.rollback.with.security.validation","name":"model versioning and rollback with security validation","description":"Maintains a complete version history of trained models with associated metadata (training data, hyperparameters, security policies, compliance status) and enables rapid rollback to previous versions. The system validates that rolled-back models meet current security and compliance requirements before allowing deployment, preventing rollback to versions that violate current policies.","intents":["I need to quickly rollback a model that was poisoned or shows unexpected behavior in production, while ensuring the rollback version meets current security standards","I want to maintain a complete audit trail of which model versions were deployed when and what security policies were in effect","I need to compare security properties of different model versions to understand how security posture has evolved"],"best_for":["Organizations deploying models in production where rapid rollback is critical","Teams with strict audit requirements that need to track model versions and security policies together","Companies concerned about model poisoning or adversarial attacks that require quick recovery"],"limitations":["Model versioning requires significant storage for large models (e.g., LLMs); requires external storage infrastructure","Rollback validation adds latency to rollback operations (5-30 seconds depending on validation complexity)","Cannot rollback to versions that violate current compliance requirements; may force upgrade to compliant version instead"],"requires":["Model artifact storage (S3, GCS, Azure Blob, or on-premises)","Model metadata tracking (included in MLCode or external)","Security policy definitions for rollback validation"],"input_types":["trained model artifacts","model metadata (training data, hyperparameters, security policies)","rollback target version"],"output_types":["model version history","rollback validation reports","deployment audit logs"],"categories":["automation-workflow","safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mlcode__cap_11","uri":"capability://data.processing.analysis.federated.learning.and.privacy.preserving.model.training","name":"federated learning and privacy-preserving model training","description":"Enables training models on distributed data without centralizing sensitive data by implementing federated learning protocols where model updates are computed locally and only aggregated centrally. The system supports differential privacy techniques to add noise to model updates, preventing reconstruction of training data from model weights, and coordinates training across heterogeneous environments (cloud, on-prem, edge devices).","intents":["I need to train models on sensitive data from multiple organizations without any organization sharing raw data with others","I want to ensure that even if someone obtains the trained model, they cannot reverse-engineer the training data using membership inference or model inversion attacks","I need to train models on data that cannot be moved due to data residency regulations, so I must bring the model to the data rather than centralizing data"],"best_for":["Consortiums or multi-party collaborations training models on sensitive data","Organizations subject to strict data residency or data minimization regulations","Companies concerned about privacy attacks on trained models (membership inference, model inversion)"],"limitations":["Federated learning introduces significant communication overhead; training time can be 5-10x longer than centralized training","Differential privacy reduces model accuracy; requires careful tuning of privacy budgets to balance privacy and utility","Requires custom training code or framework support (TensorFlow Federated, PySyft); not compatible with all training frameworks"],"requires":["Federated learning framework (TensorFlow Federated, PySyft, or custom implementation)","Network connectivity between participating organizations/devices","Support for distributed training in model training code"],"input_types":["model training code (with federated learning support)","local data (never centralized)","privacy budget parameters (epsilon, delta for differential privacy)"],"output_types":["trained model (aggregated from local updates)","privacy guarantees (differential privacy parameters)","training audit logs"],"categories":["data-processing-analysis","safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mlcode__cap_2","uri":"capability://data.processing.analysis.automated.data.masking.and.redaction.for.model.training","name":"automated data masking and redaction for model training","description":"Applies context-aware data masking rules to training datasets before they reach model training jobs, using pattern matching and semantic analysis to identify sensitive data (PII, credentials, proprietary metrics) and redact or tokenize them. The system integrates with feature stores and data loaders to intercept data at the point of access, ensuring models never see raw sensitive values while preserving statistical properties needed for model performance.","intents":["I want to train models on production data without exposing PII to data scientists or in model artifacts","I need to ensure that even if a model is stolen, it cannot be reverse-engineered to extract the sensitive training data","I want to apply different masking rules to different teams (e.g., junior data scientists see more redacted data than senior engineers)"],"best_for":["Organizations training models on sensitive data (healthcare, finance, PII-heavy datasets)","Teams with compliance requirements around data scientist access (HIPAA, PCI-DSS)","Companies concerned about model extraction attacks or data leakage through model weights"],"limitations":["Masking can reduce model performance if sensitive features are critical to model accuracy — requires careful tuning of masking rules","Pattern-based detection has false positive/negative rates; semantic analysis requires additional ML inference, adding 50-200ms per batch","Cannot mask data that is implicit in model behavior (e.g., a model trained on salary data may leak salary information through predictions)"],"requires":["Integration with data loader or feature store (Spark, Pandas, Feast, Tecton, etc.)","Definition of sensitive data patterns (regex, semantic classifiers, or custom rules)","Python 3.8+ or Spark 3.0+ for instrumentation"],"input_types":["training datasets (CSV, Parquet, database queries)","masking rule definitions (YAML/JSON)","data schema metadata"],"output_types":["masked training datasets","masking audit logs","data quality metrics (pre/post masking)"],"categories":["data-processing-analysis","safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mlcode__cap_3","uri":"capability://safety.moderation.inference.time.data.access.control.and.audit.logging","name":"inference-time data access control and audit logging","description":"Enforces fine-grained access controls on model inference requests by validating user identity, data context, and request metadata against security policies before predictions are returned. The system logs all inference requests with full context (user, timestamp, input features, output predictions) to an immutable audit trail, enabling forensic analysis and compliance reporting for regulated use cases.","intents":["I need to ensure only authorized users can query certain models, and different users should see different model outputs based on their data access level","I want to detect if a model is being queried with adversarial inputs designed to extract training data or cause harmful predictions","I need to prove to auditors that all model predictions were made by authorized users and log which data was used for each prediction"],"best_for":["Organizations deploying models in regulated industries (healthcare, finance, government)","Teams concerned about model misuse or adversarial attacks on inference endpoints","Companies with strict audit requirements (SOC2, ISO 27001, HIPAA)"],"limitations":["Access control checks add 10-50ms latency per inference request depending on policy complexity","Audit logging at scale (millions of inferences/day) requires significant storage and query infrastructure","Cannot prevent inference attacks that exploit model behavior itself (e.g., membership inference attacks); only logs access"],"requires":["Integration with model serving infrastructure (KServe, Seldon, SageMaker, custom serving)","Identity provider or authentication system (OAuth2, SAML, API keys)","Persistent audit log storage (included in MLCode or external SIEM)"],"input_types":["inference requests (JSON/protobuf)","user identity/authentication tokens","access control policies"],"output_types":["model predictions (with access control applied)","audit logs (JSON, queryable)","access denial events"],"categories":["safety-moderation","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mlcode__cap_4","uri":"capability://automation.workflow.automated.compliance.policy.generation.from.regulatory.frameworks","name":"automated compliance policy generation from regulatory frameworks","description":"Translates regulatory requirements (HIPAA, GDPR, SOC2, PCI-DSS) into executable security policies that can be deployed across ML infrastructure. The system maintains a library of compliance templates and uses natural language processing to map regulatory text to specific technical controls (data masking, encryption, access logging), reducing the manual effort of translating compliance documents into code.","intents":["I need to quickly implement HIPAA controls for a healthcare ML project without hiring a compliance consultant","I want to generate audit-ready documentation showing how my ML infrastructure meets GDPR data minimization requirements","I need to update all security policies when a new regulation is introduced or existing regulations change"],"best_for":["Enterprises in regulated industries (healthcare, finance, government) building ML systems","Organizations with limited compliance/security staff who need to move fast","Teams managing multiple compliance frameworks simultaneously (e.g., HIPAA + GDPR + SOC2)"],"limitations":["Compliance templates are generic and may not cover industry-specific or organization-specific requirements","NLP-based mapping of regulatory text to technical controls has error rates; requires manual review and adjustment","Generated policies may be overly conservative (false positives) or miss edge cases, requiring security expert review"],"requires":["Selection of applicable regulatory frameworks (HIPAA, GDPR, SOC2, PCI-DSS, etc.)","Existing MLCode deployment with policy engine","Security team review and approval of generated policies before deployment"],"input_types":["regulatory framework selection (dropdown/config)","organization metadata (industry, data types, geography)","existing security policies (optional, for augmentation)"],"output_types":["executable security policies (YAML/JSON)","compliance mapping documents (PDF/HTML)","policy implementation checklist"],"categories":["automation-workflow","safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mlcode__cap_5","uri":"capability://safety.moderation.data.poisoning.detection.and.model.input.validation","name":"data poisoning detection and model input validation","description":"Monitors training data and inference inputs for anomalies, statistical drift, and adversarial patterns that indicate data poisoning attacks. The system builds statistical baselines of normal data distributions during training and flags inputs that deviate significantly, using techniques like isolation forests, autoencoders, and statistical hypothesis testing to detect both obvious and subtle poisoning attempts.","intents":["I want to detect if someone is injecting malicious training data to degrade model performance or introduce backdoors","I need to identify inference inputs that are adversarially crafted to cause harmful or biased predictions","I want to automatically reject suspicious inputs before they reach the model to prevent inference-time attacks"],"best_for":["Organizations deploying models in adversarial environments (fraud detection, security systems, autonomous systems)","Teams with open data pipelines where data sources may be compromised","Systems where model failure has high consequences (healthcare, autonomous vehicles, financial trading)"],"limitations":["Anomaly detection has inherent false positive rates; requires tuning thresholds to balance security vs. usability","Cannot detect poisoning attacks that preserve overall data distribution (e.g., label flipping on small subset)","Requires baseline period of clean data to establish normal distributions; ineffective for new models or rapidly changing data"],"requires":["Historical clean training data to establish baseline distributions","Inference request logging (provided by MLCode inference-time access control)","Computational resources for real-time anomaly detection (GPU optional but recommended)"],"input_types":["training datasets","inference requests","data schema and feature definitions"],"output_types":["anomaly detection alerts","poisoning risk scores","rejected inference requests (with reason)"],"categories":["safety-moderation","data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mlcode__cap_6","uri":"capability://safety.moderation.model.artifact.encryption.and.secure.storage","name":"model artifact encryption and secure storage","description":"Encrypts trained model weights, checkpoints, and metadata at rest using hardware-backed encryption (HSM, KMS) and in transit using TLS 1.3. The system manages encryption keys separately from model artifacts, supports key rotation policies, and integrates with cloud KMS services (AWS KMS, Azure Key Vault, GCP Cloud KMS) to avoid storing keys in MLCode infrastructure.","intents":["I need to ensure that even if someone gains access to our model storage, they cannot extract or use the model weights","I want to rotate encryption keys regularly without re-encrypting all stored models","I need to comply with data residency requirements by ensuring models are encrypted with keys stored in specific regions"],"best_for":["Organizations with proprietary or high-value models that are targets for theft","Teams subject to data residency regulations (GDPR, CCPA, data localization laws)","Companies concerned about supply chain attacks or compromised storage infrastructure"],"limitations":["Encryption/decryption adds 50-200ms latency per model load depending on key size and HSM network latency","Key management complexity increases operational burden; requires careful key rotation and access control policies","Cannot protect against attacks that occur after model decryption (e.g., model extraction from running inference server)"],"requires":["Integration with cloud KMS (AWS KMS, Azure Key Vault, GCP Cloud KMS) or on-premises HSM","Model storage infrastructure (S3, GCS, Azure Blob, or on-premises)","Network connectivity to KMS service for key operations"],"input_types":["trained model artifacts (PyTorch, TensorFlow, ONNX, etc.)","encryption key policies (rotation schedule, access control)"],"output_types":["encrypted model artifacts","key rotation audit logs","encryption status reports"],"categories":["safety-moderation","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mlcode__cap_7","uri":"capability://automation.workflow.cross.environment.security.policy.drift.detection","name":"cross-environment security policy drift detection","description":"Continuously monitors deployed security policies across all environments and detects deviations from the intended policy state (policy drift). The system compares actual deployed configurations against the centralized policy definition, identifies which environment(s) have diverged, and generates alerts with remediation recommendations to bring drifted environments back into compliance.","intents":["I want to detect if a security policy was accidentally modified or disabled in production without going through the change management process","I need to identify which environments are out of compliance with the latest security policies and automatically remediate them","I want to track the history of policy changes across environments to audit who made changes and when"],"best_for":["Large organizations with multiple teams managing different environments","Teams with strict change control requirements (financial services, healthcare)","Organizations concerned about configuration drift and compliance violations"],"limitations":["Drift detection requires continuous monitoring, adding overhead to control plane (estimated 5-10% CPU/memory)","Cannot distinguish between intentional temporary overrides and accidental drift; requires manual review","Automated remediation may cause service disruptions if policies are critical to operations; requires careful testing"],"requires":["HexaKube agents deployed in all target environments","Centralized policy repository (Git, MLCode platform, or external)","Change notification system (webhooks, event streams) for real-time drift detection"],"input_types":["deployed policy configurations (from environments)","intended policy definitions (from central repository)","change logs and audit trails"],"output_types":["drift detection alerts","policy comparison reports","remediation recommendations"],"categories":["automation-workflow","safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mlcode__cap_8","uri":"capability://safety.moderation.role.based.and.attribute.based.access.control.for.data.and.models","name":"role-based and attribute-based access control for data and models","description":"Implements fine-grained access control using both role-based access control (RBAC) and attribute-based access control (ABAC) to restrict who can access which data, models, and features. The system evaluates access requests against policies that consider user role, data classification, data residency, model sensitivity, and contextual attributes (time of day, IP address, device type) before granting access.","intents":["I need to ensure junior data scientists can only access non-sensitive data and cannot export raw data outside the secure environment","I want to restrict access to high-risk models (e.g., models used for hiring decisions) to only authorized users and log all access","I need to enforce data residency controls so that data from EU users is never accessed by users in other regions"],"best_for":["Organizations with complex access control requirements across multiple data sensitivity levels","Teams with distributed data science teams across multiple regions or organizations","Companies subject to data residency or data localization regulations"],"limitations":["ABAC policy evaluation can be computationally expensive; requires careful policy design to avoid performance degradation","Attribute management (user attributes, data attributes) requires integration with identity and data governance systems","Policy conflicts or overly complex rules can lead to unexpected access denials; requires careful testing and documentation"],"requires":["Identity provider integration (LDAP, Active Directory, OAuth2, SAML)","Data classification and tagging system","Policy engine (included in MLCode) and policy definition language (YAML/JSON)"],"input_types":["access requests (user identity, resource, action)","access control policies (RBAC and ABAC rules)","user and data attributes"],"output_types":["access grant/deny decisions","access audit logs","policy evaluation reports"],"categories":["safety-moderation","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_mlcode__cap_9","uri":"capability://automation.workflow.automated.security.incident.response.and.remediation","name":"automated security incident response and remediation","description":"Detects security incidents (unauthorized access attempts, policy violations, data exfiltration attempts) and automatically executes remediation workflows such as revoking access, isolating affected systems, quarantining suspicious data, or triggering manual escalation. The system uses rule-based incident detection and integrates with SIEM systems and incident management platforms (PagerDuty, Splunk) for alerting and orchestration.","intents":["I want to automatically revoke access for a user who is attempting to export sensitive data outside approved channels","I need to quarantine a model that shows signs of poisoning and prevent it from being deployed until manual review is complete","I want to automatically escalate high-severity security incidents to the security team while logging all remediation actions for audit purposes"],"best_for":["Organizations with security operations centers (SOCs) that need automated incident response","Teams managing high-risk ML systems where rapid response is critical","Companies with strict incident response SLAs (e.g., must respond to incidents within 15 minutes)"],"limitations":["Automated remediation can cause service disruptions if overly aggressive; requires careful tuning of incident detection thresholds","False positives in incident detection can lead to unnecessary remediation actions; requires baseline tuning period","Cannot remediate incidents that occur outside MLCode's visibility (e.g., data exfiltration through side channels)"],"requires":["Incident detection rules (built-in templates or custom rules)","Integration with incident management platform (PagerDuty, Splunk, Datadog, etc.)","Remediation action capabilities (access revocation, model quarantine, data isolation)"],"input_types":["security events (access logs, policy violations, anomalies)","incident detection rules","remediation action definitions"],"output_types":["incident alerts","remediation action logs","incident response reports"],"categories":["automation-workflow","safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Network connectivity between MLCode control plane and target environments","Deployment permissions in target cloud/on-prem infrastructure","Existing data pipeline infrastructure (Spark, Airflow, Kubernetes, or cloud-native services)","Access to data pipeline source code or ability to inject instrumentation","Supported data frameworks (Spark, Pandas, TensorFlow, PyTorch, or cloud-native services)","Persistent storage for lineage graph (included in MLCode platform)","Model artifact storage (S3, GCS, Azure Blob, or on-premises)","Model metadata tracking (included in MLCode or external)","Security policy definitions for rollback validation","Federated learning framework (TensorFlow Federated, PySyft, or custom implementation)"],"failure_modes":["Requires pre-existing infrastructure instrumentation — cannot enforce policies on unmonitored data pipelines","Policy translation overhead may introduce 100-500ms latency per environment sync depending on policy complexity","Limited to environments where HexaKube agents can be deployed; air-gapped systems require custom integration","Requires instrumentation of data access layers — custom data loaders or proprietary data systems may require manual integration","Lineage tracking adds computational overhead to data pipelines (estimated 5-15% depending on pipeline complexity)","Cannot retroactively reconstruct lineage for historical data; only tracks lineage from deployment forward","Model versioning requires significant storage for large models (e.g., LLMs); requires external storage infrastructure","Rollback validation adds latency to rollback operations (5-30 seconds depending on validation complexity)","Cannot rollback to versions that violate current compliance requirements; may force upgrade to compliant version instead","Federated learning introduces significant communication overhead; training time can be 5-10x longer than centralized training","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.858Z","last_scraped_at":"2026-04-05T13:23:42.560Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mlcode","compare_url":"https://unfragile.ai/compare?artifact=mlcode"}},"signature":"NRrEmqYgbrgK5Kz7NZDc4an0jU0gJH9Gsf4uICWNqYiXbgWR25aeal27ISYOcSTIgVGWqOTNr3V5FgdAYEPeDA==","signedAt":"2026-06-21T09:51:12.413Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mlcode","artifact":"https://unfragile.ai/mlcode","verify":"https://unfragile.ai/api/v1/verify?slug=mlcode","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}