{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-agenshield","slug":"agenshield","name":"agenshield","type":"agent","url":"https://github.com/agen-co/agen-shield#readme","page_url":"https://unfragile.ai/agenshield","categories":["code-review-security"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-agenshield__cap_0","uri":"capability://safety.moderation.agent.action.interception.and.validation","name":"agent-action-interception-and-validation","description":"Intercepts and validates AI agent actions before execution by implementing a middleware layer that inspects tool calls, API requests, and state mutations against configurable security policies. Uses a hook-based architecture to wrap agent execution pipelines, enabling real-time inspection of intent, parameters, and side effects without modifying core agent logic.","intents":["prevent agents from executing unauthorized API calls or dangerous operations","validate that agent actions conform to business rules and compliance requirements","audit and log all agent decisions before they affect production systems","implement guardrails that stop agents from accessing sensitive data or resources"],"best_for":["teams deploying autonomous agents in production environments","enterprises requiring audit trails and compliance enforcement for AI systems","developers building multi-agent systems with shared resource constraints"],"limitations":["interception adds latency to agent execution — exact overhead depends on policy complexity and validation rules","requires explicit policy definition for each agent capability — no automatic inference of safe actions","policies are static at runtime — dynamic policy updates require agent restart or hot-reload mechanism"],"requires":["Node.js 14+ or compatible JavaScript runtime","agent framework with hook/middleware support (e.g., LangChain, AutoGPT-compatible agents)","policy definition format (JSON, YAML, or programmatic rules)"],"input_types":["agent action objects (tool name, parameters, context)","policy definitions (rules, constraints, allowlists)","execution context (user identity, resource permissions)"],"output_types":["validation result (approved/rejected with reason)","audit log entries (action, timestamp, decision, actor)","modified action (sanitized parameters if policy allows with modifications)"],"categories":["safety-moderation","agent-security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agenshield__cap_1","uri":"capability://safety.moderation.tool.call.schema.validation.with.constraint.enforcement","name":"tool-call-schema-validation-with-constraint-enforcement","description":"Validates tool/function calls against JSON schemas and enforces parameter constraints (type, range, format, allowlists) before agents invoke external APIs or tools. Implements schema-aware validation that checks not just type correctness but also business logic constraints like rate limits, resource quotas, and parameter dependencies.","intents":["ensure agents only call tools with valid, safe parameter combinations","prevent agents from requesting resources beyond allocated quotas or limits","validate that function arguments match expected schemas and business constraints","reject malformed or suspicious tool invocations before they reach external systems"],"best_for":["teams integrating agents with multiple third-party APIs or microservices","systems requiring strict input validation for compliance or security","multi-tenant platforms where agents must respect per-tenant resource limits"],"limitations":["schema validation is synchronous — complex constraint checks may add 10-50ms per validation","requires explicit schema definition for each tool — no automatic schema inference from API documentation","constraint logic is declarative but not Turing-complete — complex conditional constraints require custom validators"],"requires":["JSON Schema or compatible schema format (OpenAPI, TypeScript interfaces)","constraint definition mechanism (JSON rules, custom validator functions)","tool registry or catalog with schema metadata"],"input_types":["tool call objects (tool name, parameters, context)","JSON schemas (parameter definitions, type constraints)","constraint rules (allowlists, ranges, quotas, dependencies)"],"output_types":["validation result (valid/invalid with error details)","constraint violation report (which constraint failed, expected vs actual)","sanitized parameters (if partial validation with auto-correction is enabled)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agenshield__cap_2","uri":"capability://safety.moderation.agent.behavior.monitoring.and.anomaly.detection","name":"agent-behavior-monitoring-and-anomaly-detection","description":"Monitors agent execution patterns and detects anomalous behavior by tracking metrics like action frequency, resource consumption, error rates, and decision patterns over time. Uses statistical baselines and rule-based heuristics to identify deviations that may indicate agent malfunction, adversarial prompting, or security incidents.","intents":["detect when agents are behaving abnormally or outside expected patterns","identify potential security incidents like prompt injection or unauthorized resource access","monitor agent performance and health in production","trigger alerts or automatic remediation when anomalies are detected"],"best_for":["teams running agents in production with high autonomy","systems where agent failures or compromises could have significant impact","organizations needing real-time visibility into agent behavior"],"limitations":["anomaly detection requires baseline data — new agents have no historical context for comparison","statistical models may produce false positives in early deployment phases","detection latency depends on aggregation window — real-time detection requires short windows but may miss subtle patterns"],"requires":["metrics collection infrastructure (time-series database or event stream)","baseline establishment period (typically 24-48 hours of normal operation)","alerting system integration (webhook, message queue, or monitoring platform)"],"input_types":["agent execution events (action, timestamp, duration, result)","resource metrics (API calls, memory, compute, data accessed)","error logs and exception traces"],"output_types":["anomaly alerts (type, severity, affected agent, timestamp)","behavior reports (metrics summary, deviations from baseline)","remediation recommendations (throttle, pause, or restart agent)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agenshield__cap_3","uri":"capability://safety.moderation.resource.access.control.with.capability.binding","name":"resource-access-control-with-capability-binding","description":"Enforces fine-grained access control by binding agents to specific resources, APIs, and capabilities based on identity, role, or context. Implements a capability-based security model where agents receive a scoped set of allowed tools and resources, with enforcement at the invocation layer preventing access to unbound capabilities.","intents":["restrict agents to only the tools and resources they need for their assigned tasks","implement role-based or context-based access control for multi-agent systems","prevent privilege escalation where agents attempt to access resources beyond their scope","support multi-tenant scenarios where agents must be isolated to tenant-specific resources"],"best_for":["multi-agent systems with heterogeneous capabilities and access requirements","multi-tenant platforms where agents must be strictly isolated","enterprises with complex authorization policies and compliance requirements"],"limitations":["capability binding is static at agent initialization — dynamic capability grants require agent restart","no transitive capability delegation — agents cannot grant capabilities to other agents","requires explicit capability inventory — no automatic capability discovery from available tools"],"requires":["identity/authentication system (user, service account, or agent identity)","capability registry or manifest (list of available tools and resources)","access control policy engine (role-based, attribute-based, or custom logic)"],"input_types":["agent identity (user ID, service account, agent name)","requested capability (tool name, resource identifier)","context (tenant ID, session, request metadata)"],"output_types":["access decision (allowed/denied)","scoped capability set (list of allowed tools with parameters)","audit log entry (who accessed what, when, result)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agenshield__cap_4","uri":"capability://safety.moderation.prompt.injection.detection.and.mitigation","name":"prompt-injection-detection-and-mitigation","description":"Detects and mitigates prompt injection attacks by analyzing user inputs and agent prompts for suspicious patterns, embedded instructions, or attempts to override system prompts. Uses pattern matching, semantic analysis, and heuristics to identify injection attempts before they reach the LLM, with optional sanitization or rejection of suspicious inputs.","intents":["prevent attackers from injecting malicious instructions into agent prompts","detect when user inputs contain attempts to manipulate agent behavior","sanitize or reject suspicious inputs before they influence agent decisions","maintain agent behavior integrity even when exposed to adversarial inputs"],"best_for":["agents exposed to untrusted user input (chatbots, public APIs)","systems where prompt injection could lead to unauthorized actions or data exposure","applications requiring high confidence in agent behavior consistency"],"limitations":["detection is heuristic-based — sophisticated injection attacks may evade pattern matching","false positives possible — legitimate user inputs may be flagged as injection attempts","no guarantee of complete protection — determined attackers may find novel injection techniques"],"requires":["input preprocessing pipeline","pattern database or detection rules","optional: semantic analysis model or LLM-based detection"],"input_types":["user input text","agent system prompts","conversation history"],"output_types":["injection detection result (detected/not detected)","risk score (confidence level of injection attempt)","sanitized input (if auto-correction enabled)","audit log entry (input, detection result, action taken)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agenshield__cap_5","uri":"capability://safety.moderation.output.filtering.and.content.moderation","name":"output-filtering-and-content-moderation","description":"Filters and moderates agent outputs before they are returned to users or trigger external actions, checking for harmful content, sensitive data leakage, policy violations, or format violations. Implements a moderation pipeline that can reject, sanitize, or flag outputs based on configurable rules and optional integration with content moderation APIs.","intents":["prevent agents from returning harmful, offensive, or inappropriate content","detect and prevent accidental leakage of sensitive data in agent responses","ensure agent outputs comply with content policies and regulations","flag suspicious outputs for human review before they reach users"],"best_for":["public-facing agents or chatbots where output quality and safety are critical","systems handling sensitive data where leakage must be prevented","regulated industries (healthcare, finance) with strict content requirements"],"limitations":["moderation rules are reactive — they filter outputs after generation, not preventing generation","content moderation APIs add latency (typically 100-500ms per request)","no perfect detection — sophisticated data exfiltration or harmful content may evade filters","requires tuning of sensitivity thresholds — too strict causes false positives, too loose misses violations"],"requires":["moderation rule definitions (patterns, keywords, policies)","optional: third-party content moderation API (OpenAI Moderation, Perspective API, etc.)","output processing pipeline"],"input_types":["agent output text","structured output (JSON, tables)","context (user, intent, sensitivity level)"],"output_types":["moderation result (approved/rejected/flagged)","sanitized output (if auto-correction enabled)","moderation report (violations detected, confidence scores)","audit log entry"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agenshield__cap_6","uri":"capability://safety.moderation.audit.logging.and.compliance.reporting","name":"audit-logging-and-compliance-reporting","description":"Records comprehensive audit logs of all agent actions, decisions, and security events with immutable storage and compliance-ready reporting. Captures action details (what, who, when, why), security decisions (approved/rejected, reason), and context (user, tenant, resource) in a structured format suitable for compliance audits and forensic analysis.","intents":["maintain detailed audit trails for compliance and regulatory requirements","enable forensic analysis of agent behavior and security incidents","generate compliance reports for auditors and regulators","track accountability for agent actions and decisions"],"best_for":["regulated industries (healthcare, finance, legal) with audit requirements","enterprises with compliance obligations (SOC 2, HIPAA, GDPR, etc.)","systems where accountability and traceability are critical"],"limitations":["audit logging adds overhead — each action requires log write (typically 5-20ms per action)","log storage scales with agent activity — high-volume systems require robust log infrastructure","immutable logs are difficult to correct if errors occur — requires careful log design","compliance reporting is manual or requires custom integration with audit tools"],"requires":["structured logging infrastructure (file, database, or log aggregation service)","log schema definition (fields, formats, retention policies)","optional: log encryption and integrity verification for immutability"],"input_types":["agent actions (tool calls, decisions, state changes)","security events (validation results, access decisions, anomalies)","context (user, tenant, session, timestamp)"],"output_types":["audit log entries (structured records with all context)","compliance reports (formatted for auditors, with filtering and aggregation)","forensic data (detailed traces for incident investigation)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agenshield__cap_7","uri":"capability://safety.moderation.rate.limiting.and.quota.enforcement","name":"rate-limiting-and-quota-enforcement","description":"Enforces rate limits and resource quotas on agent actions to prevent abuse, resource exhaustion, and uncontrolled costs. Implements multiple rate-limiting strategies (token bucket, sliding window, quota-based) with per-agent, per-user, or per-resource granularity, with configurable thresholds and backoff behavior.","intents":["prevent agents from making excessive API calls or consuming unlimited resources","control costs by limiting agent usage and API call volume","protect downstream systems from being overwhelmed by agent requests","implement fair-use policies in multi-tenant or multi-agent systems"],"best_for":["systems with high-volume agent activity or cost-sensitive operations","multi-tenant platforms requiring per-tenant resource limits","APIs with rate limits that must be respected by agents"],"limitations":["rate limiting adds latency — checking quotas and enforcing backoff adds 1-5ms per action","distributed rate limiting requires shared state — difficult to implement consistently across multiple servers","quota enforcement is approximate in high-concurrency scenarios — exact quota boundaries may be exceeded slightly","no built-in cost tracking — requires integration with billing system for cost-based quotas"],"requires":["rate limiting strategy (token bucket, sliding window, quota-based)","quota configuration (limits per agent, user, resource, time window)","optional: distributed state store (Redis, DynamoDB) for multi-server deployments"],"input_types":["agent identity (agent ID, user ID, tenant ID)","action type (tool call, API request, resource access)","resource cost (if cost-based limiting)"],"output_types":["rate limit decision (allowed/rejected)","quota status (remaining quota, reset time)","backoff guidance (retry-after header, suggested delay)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agenshield__cap_8","uri":"capability://safety.moderation.agent.state.isolation.and.sandboxing","name":"agent-state-isolation-and-sandboxing","description":"Isolates agent state and execution context to prevent cross-contamination between agents, users, or tenants. Implements sandboxing at the state level (separate memory, context, variables) and optionally at the execution level (separate processes, containers) to ensure that one agent's actions or compromises do not affect others.","intents":["prevent agents from accessing or modifying state belonging to other agents or users","isolate multi-tenant agents so that one tenant's data cannot leak to another","contain the impact of a compromised or misbehaving agent","ensure agent behavior is deterministic and reproducible within its isolated context"],"best_for":["multi-tenant systems where strict data isolation is required","high-security environments where agent compromise must be contained","systems running untrusted or third-party agents"],"limitations":["state isolation adds memory overhead — each agent requires separate context storage","process/container isolation adds significant latency and resource overhead","shared resources (databases, APIs) still require access control — isolation alone is insufficient","debugging and monitoring isolated agents is more complex"],"requires":["state management system with per-agent isolation","optional: containerization or process isolation (Docker, VM, or OS-level sandboxing)","context switching and state serialization/deserialization"],"input_types":["agent identity (agent ID, user ID, tenant ID)","state data (variables, memory, context)","execution context"],"output_types":["isolated state (agent-specific variables and context)","isolation verification (confirmation that state is isolated)","state snapshots (for debugging and recovery)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agenshield__cap_9","uri":"capability://safety.moderation.policy.definition.and.management","name":"policy-definition-and-management","description":"Provides a framework for defining, versioning, and managing security policies that govern agent behavior. Supports declarative policy definitions (JSON, YAML, or domain-specific language) with version control, policy composition, and runtime policy updates without agent restart.","intents":["define security policies that govern what agents can and cannot do","version and audit policy changes for compliance and rollback","compose policies from reusable components for consistency across agents","update policies at runtime without restarting agents"],"best_for":["teams managing multiple agents with consistent security policies","organizations requiring policy versioning and audit trails","systems where policies need to be updated frequently without downtime"],"limitations":["policy language expressiveness is limited — complex conditional logic may require custom code","runtime policy updates require careful coordination — inconsistent policy versions may cause confusion","policy composition can become complex — deeply nested or interdependent policies are difficult to reason about","no built-in policy testing framework — policies must be tested manually or with custom tooling"],"requires":["policy definition format (JSON, YAML, or custom DSL)","policy storage and versioning (Git, database, or configuration service)","policy evaluation engine"],"input_types":["policy definitions (rules, constraints, allowlists)","policy metadata (version, author, effective date)","policy composition rules (inheritance, overrides)"],"output_types":["evaluated policies (effective rules for a given agent/context)","policy audit trail (changes, versions, approvals)","policy validation results (syntax errors, conflicts)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or compatible JavaScript runtime","agent framework with hook/middleware support (e.g., LangChain, AutoGPT-compatible agents)","policy definition format (JSON, YAML, or programmatic rules)","JSON Schema or compatible schema format (OpenAPI, TypeScript interfaces)","constraint definition mechanism (JSON rules, custom validator functions)","tool registry or catalog with schema metadata","metrics collection infrastructure (time-series database or event stream)","baseline establishment period (typically 24-48 hours of normal operation)","alerting system integration (webhook, message queue, or monitoring platform)","identity/authentication system (user, service account, or agent identity)"],"failure_modes":["interception adds latency to agent execution — exact overhead depends on policy complexity and validation rules","requires explicit policy definition for each agent capability — no automatic inference of safe actions","policies are static at runtime — dynamic policy updates require agent restart or hot-reload mechanism","schema validation is synchronous — complex constraint checks may add 10-50ms per validation","requires explicit schema definition for each tool — no automatic schema inference from API documentation","constraint logic is declarative but not Turing-complete — complex conditional constraints require custom validators","anomaly detection requires baseline data — new agents have no historical context for comparison","statistical models may produce false positives in early deployment phases","detection latency depends on aggregation window — real-time detection requires short windows but may miss subtle patterns","capability binding is static at agent initialization — dynamic capability grants require agent restart","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.1738780558484369,"quality":0.3,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.328Z","last_scraped_at":"2026-05-03T14:04:47.473Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":2740,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=agenshield","compare_url":"https://unfragile.ai/compare?artifact=agenshield"}},"signature":"FoVSqYDq2pzpKfz2M6zDeEhxyM/32S/7FP512oqzcwkbIdnC42xe7W1ulMsvCVGor5b4CAQZIX17dkJOxB0vCg==","signedAt":"2026-06-21T10:59:29.752Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agenshield","artifact":"https://unfragile.ai/agenshield","verify":"https://unfragile.ai/api/v1/verify?slug=agenshield","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"}}