{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-47374958","slug":"agentarmor-open-source-8-layer-security-framework-","name":"AgentArmor – open-source 8-layer security framework for AI agents","type":"framework","url":"https://github.com/Agastya910/agentarmor","page_url":"https://unfragile.ai/agentarmor-open-source-8-layer-security-framework-","categories":["code-review-security"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-47374958__cap_0","uri":"capability://safety.moderation.multi.layer.prompt.injection.detection.and.neutralization","name":"multi-layer prompt injection detection and neutralization","description":"Detects and mitigates prompt injection attacks across 8 distinct security layers using pattern matching, semantic analysis, and input sanitization techniques. Each layer targets specific attack vectors (direct injection, indirect injection, jailbreaks, token smuggling) with progressive filtering that escalates from syntax-level checks to LLM-based semantic validation, preventing malicious instructions from reaching the agent's core reasoning engine.","intents":["prevent attackers from hijacking agent behavior through crafted prompts","detect when user input contains hidden instructions targeting the underlying LLM","sanitize untrusted data before passing to agent decision-making logic","identify jailbreak attempts that try to override system instructions"],"best_for":["teams deploying AI agents in production with untrusted user input","developers building customer-facing chatbots or autonomous systems","security-conscious organizations handling sensitive data through agents"],"limitations":["detection accuracy depends on layer configuration; overly aggressive filtering may block legitimate requests","semantic analysis layers add latency (estimated 50-200ms per request depending on model size)","may not catch novel zero-day injection patterns not represented in training data","requires tuning per use case; generic configuration may have false positive/negative rates"],"requires":["Python 3.8+","access to an LLM API (OpenAI, Anthropic, or local model) for semantic validation layers","sufficient compute for running pattern matching and optional embedding-based detection"],"input_types":["text (user prompts, API inputs, chat messages)","structured data (JSON payloads with user-supplied fields)"],"output_types":["boolean (safe/unsafe classification)","structured risk assessment (layer-by-layer threat scores)","sanitized text (cleaned input safe for agent processing)"],"categories":["safety-moderation","security-hardening"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47374958__cap_1","uri":"capability://safety.moderation.agent.action.validation.and.authorization","name":"agent action validation and authorization","description":"Validates and authorizes agent-initiated actions (tool calls, API requests, state modifications) against a configurable policy engine before execution. The framework intercepts agent outputs, parses intended actions, checks them against role-based access control (RBAC) rules and action whitelists, and either permits, blocks, or requires human approval based on risk level and policy configuration.","intents":["prevent agents from calling unauthorized APIs or tools","enforce role-based permissions so agents respect user/context boundaries","require human approval for high-risk actions (data deletion, external transfers)","audit and log all agent actions for compliance and debugging"],"best_for":["enterprises deploying autonomous agents with access to critical systems","teams building agents that interact with external APIs or databases","compliance-heavy industries (finance, healthcare) requiring action auditability"],"limitations":["policy configuration is manual and error-prone; misconfigured rules can create security gaps","adds decision latency (10-50ms per action validation depending on policy complexity)","does not prevent agents from attempting unauthorized actions; only blocks execution","requires integration with external identity/authorization systems for full RBAC"],"requires":["Python 3.8+","policy definition format (YAML, JSON, or DSL) for action rules","optional: external identity provider (OAuth2, SAML) for user context"],"input_types":["agent output (tool calls, function invocations, API requests)","user context (identity, role, permissions)","policy rules (YAML/JSON configuration)"],"output_types":["authorization decision (permit/deny/require-approval)","audit log entry (action, user, timestamp, decision)","structured response (approved action or error message)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47374958__cap_2","uri":"capability://safety.moderation.output.content.filtering.and.redaction","name":"output content filtering and redaction","description":"Filters and redacts sensitive information from agent outputs before returning to users, using pattern matching, PII detection, and semantic analysis to identify and mask credentials, personal data, internal IDs, and other sensitive content. The framework supports configurable redaction rules, regex patterns, and LLM-based semantic detection to prevent accidental data leakage through agent responses.","intents":["prevent agents from leaking API keys, credentials, or internal secrets in responses","redact personally identifiable information (PII) before returning results to users","mask internal system details (database names, internal IDs) from external-facing responses","ensure compliance with data protection regulations (GDPR, HIPAA) by sanitizing outputs"],"best_for":["customer-facing AI applications handling sensitive user data","enterprises with strict data governance and compliance requirements","teams building agents that access internal systems but serve external users"],"limitations":["pattern-based detection (regex) has high false positive rates for context-dependent data","semantic redaction adds 100-300ms latency per response","cannot redact information the agent was not trained to recognize as sensitive","over-aggressive redaction may render outputs unintelligible or useless"],"requires":["Python 3.8+","redaction rule definitions (regex patterns, PII categories, custom rules)","optional: LLM API for semantic detection of sensitive content"],"input_types":["agent output text","structured data (JSON, CSV) from agent responses","redaction rule configuration"],"output_types":["redacted text (with sensitive data masked or removed)","redaction report (what was redacted, where, why)","original + redacted versions (for audit trails)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47374958__cap_3","uri":"capability://safety.moderation.rate.limiting.and.resource.quota.enforcement","name":"rate limiting and resource quota enforcement","description":"Enforces rate limits and resource quotas on agent execution to prevent abuse, DoS attacks, and runaway costs. The framework tracks agent invocations, token consumption, API calls, and compute time per user/session/agent, enforcing configurable limits and throttling or rejecting requests that exceed thresholds. Supports sliding window rate limiting, token bucket algorithms, and per-resource quotas.","intents":["prevent users from overwhelming the system with excessive agent invocations","control LLM API costs by limiting token consumption per user or time period","protect against denial-of-service attacks targeting the agent infrastructure","ensure fair resource allocation across multiple concurrent users or agents"],"best_for":["multi-tenant SaaS platforms hosting AI agents","teams with limited LLM API budgets needing cost control","public-facing agent services vulnerable to abuse"],"limitations":["requires distributed state (Redis, etc.) for accurate rate limiting across multiple servers","quota enforcement adds 5-20ms latency per request for state lookup/update","does not prevent resource exhaustion from legitimate high-volume usage","quota configuration is manual; no automatic scaling or adaptive limits"],"requires":["Python 3.8+","optional: Redis or similar distributed cache for multi-instance deployments","quota configuration (requests/minute, tokens/day, compute time limits, etc.)"],"input_types":["agent invocation request (user ID, agent ID, session context)","quota configuration (limits per user, per agent, per time window)"],"output_types":["boolean (request allowed/rejected)","quota status (remaining quota, reset time)","error response (rate limit exceeded, quota exhausted)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47374958__cap_4","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 that may indicate compromise, misconfiguration, or drift from intended behavior. The framework tracks metrics like action frequency, tool usage patterns, response latency, error rates, and semantic drift, comparing against baseline profiles and flagging deviations using statistical methods and ML-based anomaly detection.","intents":["detect when an agent has been compromised or is behaving unexpectedly","identify configuration drift or unintended behavior changes over time","spot performance degradation or resource exhaustion issues early","generate alerts for security teams when agent behavior deviates from baseline"],"best_for":["teams running long-lived autonomous agents in production","security operations centers (SOCs) monitoring AI systems","organizations with strict behavioral compliance requirements"],"limitations":["requires historical baseline data to establish normal behavior; new agents have no baseline","anomaly detection models can have high false positive rates in early deployment","does not automatically remediate detected anomalies; requires human intervention","latency overhead for continuous monitoring (50-200ms per execution depending on metrics collected)"],"requires":["Python 3.8+","time-series database or metrics store (Prometheus, InfluxDB, etc.) for historical data","optional: ML library for anomaly detection (scikit-learn, isolation forests, etc.)"],"input_types":["agent execution telemetry (actions, tools called, latency, errors)","baseline behavior profile (historical patterns)","anomaly detection configuration (thresholds, sensitivity)"],"output_types":["anomaly alerts (deviation detected, severity, metrics)","behavior report (current vs. baseline patterns)","metrics dashboard (action frequency, tool usage, error rates over time)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47374958__cap_5","uri":"capability://safety.moderation.context.and.memory.isolation","name":"context and memory isolation","description":"Isolates agent context and memory to prevent cross-contamination between concurrent agent instances, users, or sessions. The framework enforces strict separation of execution contexts, ensuring that one agent's state, memory, and cached data cannot leak into another agent's execution. Implements context managers, thread-local storage, and optional process-level isolation for high-security deployments.","intents":["prevent one user's data or conversation history from leaking to another user","ensure concurrent agent instances don't interfere with each other's state","isolate sensitive data in memory so it's not accessible across context boundaries","enable safe multi-tenant deployments where agents serve different customers"],"best_for":["multi-tenant SaaS platforms with strict data isolation requirements","teams handling sensitive or regulated data (healthcare, finance)","deployments where agents process data from competing organizations"],"limitations":["process-level isolation adds significant overhead (100-500ms per agent invocation)","context isolation is language/runtime dependent; not all frameworks support it equally","does not prevent side-channel attacks or timing-based information leakage","requires careful integration with external services (databases, APIs) to maintain isolation"],"requires":["Python 3.8+","optional: containerization (Docker) or process isolation for high-security deployments","careful configuration of memory limits and resource constraints per context"],"input_types":["agent execution request with user/session context","isolation policy configuration (context scope, memory limits)"],"output_types":["isolated execution environment","context-scoped memory and state","isolation verification report"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47374958__cap_6","uri":"capability://safety.moderation.model.and.api.provider.verification","name":"model and api provider verification","description":"Verifies the authenticity and integrity of LLM responses and API calls to prevent man-in-the-middle attacks, model substitution, or response tampering. The framework validates cryptographic signatures on API responses, checks model identity, and verifies that responses come from expected providers using certificate pinning, response signing, and optional hardware attestation.","intents":["ensure agent responses come from the expected LLM provider, not a compromised or spoofed service","detect if an attacker has substituted a different model or intercepted API responses","verify response integrity so agents can trust the data they receive from external APIs","maintain chain of custody for audit trails in regulated environments"],"best_for":["high-security deployments where model/API authenticity is critical","regulated industries (finance, healthcare) requiring verified audit trails","teams operating in untrusted network environments"],"limitations":["requires support from LLM providers for response signing (not all providers support this)","certificate pinning adds operational complexity (certificate rotation, management)","adds 20-50ms latency per API call for signature verification","does not prevent attacks at the application level (e.g., agent logic bugs)"],"requires":["Python 3.8+","LLM provider support for response signing or certificate pinning","certificate management infrastructure for certificate pinning"],"input_types":["LLM API responses (with optional cryptographic signatures)","provider certificates or public keys","verification policy configuration"],"output_types":["verification result (authentic/tampered/unverifiable)","verification report (provider identity, signature status, timestamp)","error response if verification fails"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47374958__cap_7","uri":"capability://planning.reasoning.explainability.and.decision.tracing","name":"explainability and decision tracing","description":"Provides detailed tracing and explainability for agent decisions, showing which inputs, rules, and reasoning steps led to specific actions or outputs. The framework logs decision paths through the security layers, captures reasoning chains from the LLM, and generates human-readable explanations of why certain actions were approved, denied, or flagged. Supports integration with explainability frameworks (LIME, SHAP) for model-agnostic explanations.","intents":["understand why an agent took a specific action or made a particular decision","debug security layer decisions (why was this input flagged as injection?)","generate audit trails and compliance reports explaining agent behavior","build user trust by explaining agent reasoning in human-readable terms"],"best_for":["compliance-heavy industries requiring detailed audit trails","teams debugging agent behavior or security layer false positives","customer-facing applications where transparency builds trust","research teams studying agent behavior and decision-making"],"limitations":["detailed tracing adds significant logging overhead (10-50% performance impact)","explanations can be verbose and difficult to interpret for complex decision chains","does not explain why the underlying LLM chose a particular response","requires careful configuration to avoid logging sensitive data in traces"],"requires":["Python 3.8+","logging infrastructure (file, database, or centralized logging service)","optional: explainability libraries (LIME, SHAP) for model-agnostic explanations"],"input_types":["agent execution trace (inputs, decisions, outputs)","security layer decisions (layer name, rule matched, action taken)","LLM reasoning chain (if available from provider)"],"output_types":["detailed execution trace (JSON or structured format)","human-readable explanation (markdown or natural language)","audit report (compliance-ready format with timestamps and signatures)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47374958__cap_8","uri":"capability://safety.moderation.configuration.validation.and.policy.enforcement","name":"configuration validation and policy enforcement","description":"Validates security configuration at deployment time and enforces policy compliance throughout the agent lifecycle. The framework checks configuration files for security misconfigurations (disabled layers, overly permissive rules, weak quotas), validates policy definitions against a schema, and continuously monitors for policy drift or unauthorized changes. Supports policy-as-code with version control and approval workflows.","intents":["catch security misconfigurations before deployment (e.g., disabled security layers)","enforce organizational security policies across all deployed agents","prevent unauthorized changes to security policies or configurations","maintain compliance by ensuring configurations meet regulatory requirements"],"best_for":["large organizations with centralized security governance","teams using infrastructure-as-code and GitOps workflows","regulated industries requiring policy audit trails and approval workflows"],"limitations":["requires upfront investment in policy definition and schema design","policy validation is static; cannot catch runtime policy violations without monitoring","does not prevent misconfigured policies that are technically valid but semantically wrong","requires integration with version control and CI/CD systems for full effectiveness"],"requires":["Python 3.8+","policy definition format (YAML, JSON, or DSL)","optional: version control system (Git) for policy versioning","optional: CI/CD system for policy validation and approval workflows"],"input_types":["security configuration files (YAML, JSON)","policy definitions","schema definitions for validation"],"output_types":["validation report (pass/fail, violations found)","policy compliance report (which policies are enforced, which are violated)","remediation suggestions (how to fix violations)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","access to an LLM API (OpenAI, Anthropic, or local model) for semantic validation layers","sufficient compute for running pattern matching and optional embedding-based detection","policy definition format (YAML, JSON, or DSL) for action rules","optional: external identity provider (OAuth2, SAML) for user context","redaction rule definitions (regex patterns, PII categories, custom rules)","optional: LLM API for semantic detection of sensitive content","optional: Redis or similar distributed cache for multi-instance deployments","quota configuration (requests/minute, tokens/day, compute time limits, etc.)","time-series database or metrics store (Prometheus, InfluxDB, etc.) for historical data"],"failure_modes":["detection accuracy depends on layer configuration; overly aggressive filtering may block legitimate requests","semantic analysis layers add latency (estimated 50-200ms per request depending on model size)","may not catch novel zero-day injection patterns not represented in training data","requires tuning per use case; generic configuration may have false positive/negative rates","policy configuration is manual and error-prone; misconfigured rules can create security gaps","adds decision latency (10-50ms per action validation depending on policy complexity)","does not prevent agents from attempting unauthorized actions; only blocks execution","requires integration with external identity/authorization systems for full RBAC","pattern-based detection (regex) has high false positive rates for context-dependent data","semantic redaction adds 100-300ms latency per response","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36,"quality":0.28,"ecosystem":0.46,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":"2026-05-04T08:09:59.925Z","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=agentarmor-open-source-8-layer-security-framework-","compare_url":"https://unfragile.ai/compare?artifact=agentarmor-open-source-8-layer-security-framework-"}},"signature":"ui3PTZebjOLWFmazgHyCEzMR1BOr26Om7Wb9w0TNrMQNPDh/DEUgG0ZeC8E5dzB3C1j8ZnI+MaNHncuQTAmXBQ==","signedAt":"2026-06-21T03:08:18.161Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agentarmor-open-source-8-layer-security-framework-","artifact":"https://unfragile.ai/agentarmor-open-source-8-layer-security-framework-","verify":"https://unfragile.ai/api/v1/verify?slug=agentarmor-open-source-8-layer-security-framework-","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"}}