{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"lakera-guard","slug":"lakera-guard","name":"Lakera Guard","type":"api","url":"https://www.lakera.ai","page_url":"https://unfragile.ai/lakera-guard","categories":["code-review-security","deployment-infra","model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"lakera-guard__cap_0","uri":"capability://safety.moderation.real.time.prompt.injection.detection.with.sub.50ms.latency","name":"real-time prompt injection detection with sub-50ms latency","description":"Analyzes incoming prompts and user inputs in real-time to detect prompt injection attacks before they reach the LLM, using a neural model trained on the world's largest prompt injection dataset. The API processes requests synchronously with claimed sub-50ms latency, enabling inline deployment in production LLM pipelines without noticeable user-facing delay. Detection operates model-agnostically across any LLM backend (OpenAI, Anthropic, open-source, etc.) by analyzing prompt structure and semantic intent rather than model-specific artifacts.","intents":["Prevent attackers from injecting malicious instructions into user prompts before they reach my LLM","Detect prompt injection attempts in real-time without adding significant latency to my application","Protect my LLM application regardless of which underlying model I'm using"],"best_for":["Teams building production LLM applications with user-facing input","Security-conscious organizations deploying chatbots or AI agents in regulated industries","Developers integrating LLM APIs into existing applications where latency is critical"],"limitations":["Sub-50ms latency claim not independently verified; actual performance depends on payload size and network conditions","No documented false negative rate (missed injection attacks); only claims 0.01% false positive rate","Detection quality depends on training data coverage; novel injection techniques not in training set may evade detection","No information on how detection adapts to different prompt formats (structured JSON, markdown, code blocks, etc.)"],"requires":["API key from Lakera (authentication mechanism not documented)","Network connectivity to Lakera's SaaS endpoint","Integration into request pipeline before LLM call (synchronous processing)"],"input_types":["text (user prompts, chat messages)","code (if embedded in prompts)"],"output_types":["boolean (injection detected: true/false)","risk score (numeric confidence level, format unknown)","detection category (prompt_injection, jailbreak, etc.)"],"categories":["safety-moderation","security-threat-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lakera-guard__cap_1","uri":"capability://safety.moderation.jailbreak.attempt.detection.and.prevention","name":"jailbreak attempt detection and prevention","description":"Identifies and blocks jailbreak prompts—carefully crafted inputs designed to circumvent an LLM's safety guidelines—by analyzing prompt semantics, role-play framing, and instruction-override patterns. The detection model recognizes common jailbreak techniques (e.g., 'pretend you are an unrestricted AI', 'ignore your guidelines', hypothetical scenarios designed to elicit unsafe content) and flags them before the prompt reaches the LLM, preventing the LLM from being manipulated into generating harmful content.","intents":["Block jailbreak attempts that try to make my LLM ignore its safety guidelines","Prevent users from using role-play or hypothetical framing to extract unsafe content","Maintain consistent safety posture across my LLM application without relying solely on the model's built-in guardrails"],"best_for":["Public-facing chatbot applications where adversarial users actively attempt jailbreaks","Organizations with strict content policies (financial services, healthcare, government)","Teams deploying open-source LLMs that lack robust built-in safety mechanisms"],"limitations":["Jailbreak detection is adversarial; sophisticated new techniques may not be detected until training data is updated","No documented update frequency for jailbreak pattern detection; unclear how quickly new techniques are incorporated","May produce false positives on legitimate role-play or creative writing use cases","Detection effectiveness varies by language; claims 100+ language support but no per-language accuracy metrics provided"],"requires":["API key from Lakera","Integration into request pipeline before LLM inference","Acceptance that some legitimate creative prompts may be flagged"],"input_types":["text (user prompts with potential jailbreak framing)"],"output_types":["boolean (jailbreak detected: true/false)","risk score (numeric confidence, format unknown)","jailbreak technique category (if available)"],"categories":["safety-moderation","security-threat-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lakera-guard__cap_10","uri":"capability://safety.moderation.horizontal.threat.policy.control.across.multiple.llm.applications","name":"horizontal threat policy control across multiple llm applications","description":"Enables centralized threat policy management across multiple LLM applications and deployments, allowing security teams to define threat policies once and apply them consistently across all applications without per-application configuration. Policies can be updated globally without redeploying applications, enabling rapid response to emerging threats or policy changes. This provides a control plane for LLM security across an organization's entire LLM portfolio.","intents":["Define threat policies once and apply them consistently across all my LLM applications","Update threat detection policies globally without redeploying individual applications","Enforce organization-wide security standards across multiple LLM deployments","Audit and monitor threat detection across all applications from a central dashboard"],"best_for":["Organizations with multiple LLM applications that need consistent security policies","Enterprise teams managing LLM security across multiple teams or business units","Applications requiring rapid policy updates in response to emerging threats","Teams needing centralized visibility into threat detection across all LLM deployments"],"limitations":["Policy management interface and capabilities not documented; unclear what policies can be configured","No information on policy versioning, rollback, or audit trails","Unclear how policy changes are propagated to applications; latency between policy update and enforcement unknown","No documented support for per-application policy overrides or exceptions","No information on policy conflict resolution if multiple policies apply to same threat"],"requires":["API key from Lakera","Access to policy management interface (web dashboard, API, or other)","Integration of all LLM applications with Lakera Guard API"],"input_types":["policy definitions (format not documented)"],"output_types":["threat detection results enforcing defined policies"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lakera-guard__cap_11","uri":"capability://safety.moderation.threat.detection.for.both.user.inputs.and.llm.outputs","name":"threat detection for both user inputs and llm outputs","description":"Provides bidirectional threat detection that scans both user inputs (before they reach the LLM) and LLM outputs (before they're returned to users). This dual-direction approach prevents both adversarial inputs (prompt injection, jailbreaks) and harmful outputs (toxic content, PII leakage from the LLM's training data). The API can be called at two points in the request/response pipeline: before LLM inference (to protect the LLM) and after LLM inference (to protect users).","intents":["Prevent adversarial inputs from reaching my LLM","Detect when my LLM generates harmful content before returning it to users","Protect against both adversarial attacks and model-generated harms","Monitor LLM output quality and safety in production"],"best_for":["Applications where both input and output safety are critical","Public-facing LLM applications with user-generated content","Organizations needing comprehensive threat coverage (input + output)","Teams monitoring LLM output quality and safety"],"limitations":["Requires two API calls per request (input check + output check), doubling latency overhead","Output detection may be less effective for subtle harms (e.g., biased recommendations, subtle misinformation)","No documented support for streaming outputs; unclear how to detect threats in real-time LLM streaming","Output detection depends on LLM generating the harmful content; can't prevent harms the LLM doesn't generate"],"requires":["API key from Lakera","Integration at two points in request/response pipeline (before and after LLM call)","Handling of output threats (regenerate, filter, log, etc.)"],"input_types":["text (user inputs and LLM outputs)"],"output_types":["threat detection results for both inputs and outputs"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lakera-guard__cap_2","uri":"capability://safety.moderation.toxic.content.detection.and.filtering","name":"toxic content detection and filtering","description":"Analyzes user inputs and LLM outputs for toxic, abusive, hateful, or otherwise harmful language across 100+ languages. The detection model identifies profanity, slurs, harassment, threats, and other content that violates community standards or platform policies. Operates in real-time with sub-50ms latency, allowing toxic content to be flagged, filtered, or logged before it reaches users or is stored in application logs.","intents":["Detect and filter toxic or abusive language in user inputs before they're processed","Monitor LLM outputs for toxic content before they're shown to end users","Maintain a safe community environment by automatically flagging harmful speech"],"best_for":["Community platforms and social applications with user-generated content","Customer support chatbots that need to detect abusive customer interactions","Moderation teams that need automated pre-screening before human review","Multi-language applications serving global audiences"],"limitations":["Toxicity detection is culturally and contextually sensitive; false positives on sarcasm, reclaimed language, or context-dependent speech","No documented per-language accuracy; claims 100+ language support but no breakdown of detection quality by language","Toxicity definitions vary by platform; API does not appear to support custom toxicity policies or thresholds","May miss subtle forms of harassment, microaggressions, or context-dependent toxicity"],"requires":["API key from Lakera","Integration into input/output pipeline","Decision logic for how to handle flagged content (block, log, require review, etc.)"],"input_types":["text (user inputs, LLM outputs, chat messages)"],"output_types":["boolean (toxic content detected: true/false)","toxicity score (numeric confidence, format unknown)","toxicity category (profanity, hate speech, harassment, etc., if available)"],"categories":["safety-moderation","content-filtering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lakera-guard__cap_3","uri":"capability://safety.moderation.personally.identifiable.information.pii.leakage.detection","name":"personally identifiable information (pii) leakage detection","description":"Detects and flags the presence of sensitive personally identifiable information (PII) in user inputs and LLM outputs, including email addresses, phone numbers, credit card numbers, social security numbers, names, addresses, and other regulated data. The detection model uses pattern matching and semantic analysis to identify PII across multiple formats and languages, enabling applications to prevent accidental exposure of sensitive data in logs, outputs, or external integrations.","intents":["Prevent users from accidentally submitting PII (credit card numbers, SSNs, etc.) to my LLM","Detect when my LLM outputs contain PII that shouldn't be exposed to end users","Ensure compliance with data protection regulations (GDPR, CCPA, HIPAA) by preventing PII leakage","Audit and log PII exposure incidents for compliance and security investigations"],"best_for":["Financial services and fintech applications handling payment data","Healthcare applications processing patient information","Organizations subject to GDPR, CCPA, HIPAA, or other data protection regulations","Customer support systems where users may inadvertently share sensitive data"],"limitations":["Pattern-based PII detection can produce false positives (e.g., valid test credit card numbers, fictional SSNs in examples)","Context-dependent PII (e.g., a number that is PII in one context but not another) may not be correctly classified","No documented support for custom PII types or domain-specific sensitive data (e.g., medical record numbers, passport IDs)","Detection quality varies by PII type; some formats (credit cards, SSNs) are easier to detect than others (names, addresses)"],"requires":["API key from Lakera","Integration into input/output pipeline","Compliance framework defining which PII types must be blocked vs. logged"],"input_types":["text (user inputs, LLM outputs, chat messages)"],"output_types":["boolean (PII detected: true/false)","PII type detected (email, phone, credit_card, ssn, etc.)","PII location/span (if available, for redaction)","risk score (numeric confidence, format unknown)"],"categories":["safety-moderation","data-protection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lakera-guard__cap_4","uri":"capability://safety.moderation.model.agnostic.threat.detection.across.heterogeneous.llm.backends","name":"model-agnostic threat detection across heterogeneous llm backends","description":"Provides unified threat detection (prompt injection, jailbreaks, toxic content, PII) that works identically across any LLM backend—OpenAI, Anthropic, open-source models, custom fine-tuned models, or multi-model ensembles. The detection operates at the input/output level rather than relying on model-specific safety mechanisms, enabling consistent security posture regardless of which LLM provider or version is used. This allows teams to switch LLM providers or use multiple models in parallel without reconfiguring security policies.","intents":["Apply consistent security policies across multiple LLM providers in my application","Switch between LLM providers (e.g., OpenAI to Anthropic) without changing my security infrastructure","Use multiple LLMs in parallel (ensemble, fallback, A/B testing) with unified threat detection","Protect against threats that exploit model-specific vulnerabilities without needing model-specific defenses"],"best_for":["Teams using multiple LLM providers or planning to migrate between providers","Organizations deploying LLM ensembles or multi-model fallback strategies","Enterprises using open-source LLMs alongside commercial APIs","Applications that need consistent security regardless of underlying model changes"],"limitations":["Model-agnostic detection may miss threats that exploit model-specific vulnerabilities (e.g., prompt injection techniques tailored to GPT-4's training data)","No documented testing against all LLM models; claims 'model agnostic' but no list of tested/supported models provided","Detection quality may vary based on how different models interpret prompts; semantic analysis may not capture model-specific attack vectors","Does not provide model-specific safety features (e.g., OpenAI's system prompts, Anthropic's Constitutional AI) as fallback"],"requires":["API key from Lakera","Integration into request/response pipeline before/after LLM calls","Acceptance that model-agnostic detection may not catch all model-specific attacks"],"input_types":["text (prompts and outputs from any LLM)"],"output_types":["threat detection results (injection, jailbreak, toxic, PII flags)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lakera-guard__cap_5","uri":"capability://tool.use.integration.synchronous.api.based.threat.detection.with.inline.integration","name":"synchronous api-based threat detection with inline integration","description":"Provides threat detection via a synchronous REST API that integrates directly into request/response pipelines, enabling inline security checks without asynchronous processing or external queues. The API accepts a prompt or text input and returns threat detection results (injection, jailbreak, toxic, PII flags) within sub-50ms, allowing the application to make immediate allow/block decisions before passing data to the LLM or returning it to users. Integration is straightforward: call the API before LLM inference or after LLM output generation, and handle the response synchronously.","intents":["Check user inputs for threats in real-time before sending them to my LLM","Scan LLM outputs for threats before returning them to users","Make immediate allow/block decisions based on threat detection results","Integrate threat detection into my request pipeline without adding asynchronous complexity"],"best_for":["Synchronous request/response applications (REST APIs, web applications, chatbots)","Teams that need immediate threat detection results without queuing or async processing","Applications where latency is critical and sub-50ms overhead is acceptable","Developers preferring simple synchronous integration over complex async pipelines"],"limitations":["Synchronous API calls add latency to every request; sub-50ms claim may not hold for all payload sizes or network conditions","No documented support for batch processing or async detection; each request requires a separate API call","No streaming support documented; large prompts may require multiple API calls or truncation","Rate limiting and quota policies not documented; unclear how many requests per second are supported","No documented retry logic or circuit breaker patterns; application must handle API failures"],"requires":["API key from Lakera","HTTP client library (Python requests, Node.js fetch, etc.)","Integration code to call API before/after LLM calls","Error handling for API failures (timeouts, rate limits, service unavailability)"],"input_types":["text (prompts, outputs, chat messages)"],"output_types":["JSON response with threat detection flags (format not documented)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lakera-guard__cap_6","uri":"capability://safety.moderation.multilingual.threat.detection.across.100.languages","name":"multilingual threat detection across 100+ languages","description":"Detects prompt injection, jailbreaks, toxic content, and PII across 100+ languages using a single unified model rather than language-specific classifiers. The detection model is trained on multilingual data and can identify threats in any supported language without requiring language detection or separate language-specific pipelines. This enables global applications to apply consistent security policies across all user languages without managing multiple detection models or language-specific rules.","intents":["Detect threats in user inputs regardless of which language they're written in","Apply consistent security policies across my global user base without language-specific configuration","Avoid the complexity of managing separate threat detection models for each language","Detect threats that span multiple languages or use language-mixing to evade detection"],"best_for":["Global applications serving users in multiple languages","Platforms with user-generated content in diverse languages","Organizations expanding internationally and needing consistent security","Applications where language detection overhead is unacceptable"],"limitations":["No documented list of supported languages; claims 100+ but specific languages not specified","Detection accuracy likely varies significantly by language; no per-language accuracy metrics provided","Multilingual model may be less accurate than language-specific models for some languages","No documented support for language-specific threat patterns (e.g., language-specific slurs, culturally-specific jailbreaks)","Unclear how model handles code-switching (mixing multiple languages in single prompt)"],"requires":["API key from Lakera","Acceptance that detection quality may vary by language","No language detection or preprocessing required (API handles automatically)"],"input_types":["text in any of 100+ supported languages"],"output_types":["threat detection results (language-independent)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lakera-guard__cap_7","uri":"capability://safety.moderation.production.scale.threat.detection.with.claimed.0.01.false.positive.rate","name":"production-scale threat detection with claimed 0.01% false positive rate","description":"Designed for production deployment in high-volume LLM applications with claimed 0.01% false positive rate, meaning only 1 in 10,000 benign inputs are incorrectly flagged as threats. The detection model is optimized for precision (minimizing false positives that block legitimate users) while maintaining recall (catching actual threats). The API claims to 'scale effortlessly from zero to hundreds of prompts per second', suggesting horizontal scalability and load balancing for production traffic.","intents":["Deploy threat detection in production without blocking legitimate users due to false positives","Scale threat detection to handle hundreds of requests per second","Maintain high precision (low false positive rate) while catching actual threats","Monitor threat detection accuracy in production and adjust policies based on real-world data"],"best_for":["Production LLM applications with high user volume and strict SLAs","Organizations where false positives have significant user experience impact","Teams deploying threat detection at scale (hundreds of requests per second)","Applications where security must not compromise user experience"],"limitations":["0.01% false positive rate is claimed but not independently verified; actual rate may vary by threat type and language","No documented false negative rate (missed threats); unclear how many actual threats are missed","Scalability claim ('hundreds of prompts per second') not quantified; unclear if this is per-instance, per-region, or global","No documented SLA, uptime guarantee, or performance degradation under load","No information on how false positive rate is measured or monitored in production"],"requires":["API key from Lakera","Production infrastructure to handle API calls at scale","Monitoring and alerting to track false positive rate in production","Feedback loop to tune detection thresholds based on real-world data"],"input_types":["text (prompts, outputs, chat messages)"],"output_types":["threat detection results with confidence scores"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lakera-guard__cap_8","uri":"capability://safety.moderation.context.aware.threat.detection.with.risk.quantification","name":"context-aware threat detection with risk quantification","description":"Detects threats with contextual awareness by analyzing not just the presence of suspicious patterns but the intent and context in which they appear. The detection model returns risk scores (numeric confidence levels) rather than binary flags, enabling applications to implement graduated responses (warn user, require confirmation, block) based on threat severity. This allows legitimate use cases (e.g., discussing security vulnerabilities, creative writing with mature themes) to proceed with warnings rather than being blocked outright.","intents":["Distinguish between actual threats and legitimate use cases that happen to contain suspicious patterns","Implement graduated threat responses (warn, require confirmation, block) based on risk severity","Allow legitimate security discussions, creative writing, and educational content without false blocking","Quantify threat severity for logging, monitoring, and compliance audits"],"best_for":["Applications where false positives have high user experience cost","Platforms supporting legitimate use cases that contain threat-like patterns (security research, creative writing, education)","Teams implementing graduated security responses rather than binary allow/block","Organizations needing quantified threat severity for compliance and auditing"],"limitations":["Risk score format and scale not documented; unclear if scores are 0-1, 0-100, or other range","No documented guidance on risk score thresholds for different threat types","Context-aware detection may miss threats in ambiguous contexts or novel attack patterns","No information on how context is extracted or what contextual signals are used","Unclear how context-awareness performs across different languages and cultural contexts"],"requires":["API key from Lakera","Application logic to interpret risk scores and implement graduated responses","Tuning of risk score thresholds based on application-specific tolerance for false positives/negatives"],"input_types":["text with sufficient context (full prompt, conversation history if available)"],"output_types":["threat detection flags with risk scores (numeric confidence levels)","threat category (if available)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lakera-guard__cap_9","uri":"capability://safety.moderation.real.time.threat.adaptation.without.manual.model.updates","name":"real-time threat adaptation without manual model updates","description":"Claims to adapt threat detection in real-time to emerging attack patterns without requiring manual model retraining or deployment of new model versions. The detection system observes new threat patterns in production traffic and incorporates them into detection logic automatically, enabling the system to defend against zero-day prompt injection techniques and novel jailbreak methods as they emerge. This contrasts with static models that require periodic retraining and deployment cycles.","intents":["Defend against novel prompt injection and jailbreak techniques without waiting for model updates","Automatically adapt threat detection to emerging attack patterns in production","Reduce the time between threat discovery and deployment of detection for that threat","Maintain security posture against evolving adversarial techniques"],"best_for":["Organizations deploying LLMs in adversarial environments (public-facing chatbots, red-teaming)","Teams that can't afford the latency of manual model retraining and deployment cycles","Applications facing novel or rapidly-evolving threat patterns"],"limitations":["Real-time adaptation mechanism not documented; unclear how new patterns are detected, validated, and incorporated","No information on adaptation latency; unclear how quickly new threats are detected and deployed","Risk of false positive feedback loops; if benign patterns are misclassified as threats, adaptation could amplify false positives","No documented safeguards against adversarial poisoning of the adaptation mechanism","Unclear how adaptation is validated before deployment; risk of degrading detection quality"],"requires":["API key from Lakera","Trust in Lakera's adaptation mechanism and safeguards","Monitoring of detection quality to catch degradation from adaptation"],"input_types":["text (production traffic used for adaptation)"],"output_types":["threat detection results (updated in real-time based on adaptation)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lakera-guard__headline","uri":"capability://safety.moderation.real.time.prompt.injection.and.content.safety.api","name":"real-time prompt injection and content safety api","description":"Lakera Guard is a real-time API designed to detect and prevent prompt injection, jailbreaks, toxic content, and PII leakage in LLM applications, ensuring safe deployment of AI models.","intents":["best API for prompt injection prevention","API for toxic content detection","real-time safety API for LLMs","best API for PII leakage prevention","API to secure AI applications"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":60,"verified":false,"data_access_risk":"high","permissions":["API key from Lakera (authentication mechanism not documented)","Network connectivity to Lakera's SaaS endpoint","Integration into request pipeline before LLM call (synchronous processing)","API key from Lakera","Integration into request pipeline before LLM inference","Acceptance that some legitimate creative prompts may be flagged","Access to policy management interface (web dashboard, API, or other)","Integration of all LLM applications with Lakera Guard API","Integration at two points in request/response pipeline (before and after LLM call)","Handling of output threats (regenerate, filter, log, etc.)"],"failure_modes":["Sub-50ms latency claim not independently verified; actual performance depends on payload size and network conditions","No documented false negative rate (missed injection attacks); only claims 0.01% false positive rate","Detection quality depends on training data coverage; novel injection techniques not in training set may evade detection","No information on how detection adapts to different prompt formats (structured JSON, markdown, code blocks, etc.)","Jailbreak detection is adversarial; sophisticated new techniques may not be detected until training data is updated","No documented update frequency for jailbreak pattern detection; unclear how quickly new techniques are incorporated","May produce false positives on legitimate role-play or creative writing use cases","Detection effectiveness varies by language; claims 100+ language support but no per-language accuracy metrics provided","Policy management interface and capabilities not documented; unclear what policies can be configured","No information on policy versioning, rollback, or audit trails","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.35,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"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=lakera-guard","compare_url":"https://unfragile.ai/compare?artifact=lakera-guard"}},"signature":"kRwb3qHLTtebuigPIdvQSmd7H2lcF9v72GdE193mKorYiA6M3yw4knW/EhefuHVeRQPjXqLXmu91ghXcWGi5Bg==","signedAt":"2026-06-20T02:30:07.680Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lakera-guard","artifact":"https://unfragile.ai/lakera-guard","verify":"https://unfragile.ai/api/v1/verify?slug=lakera-guard","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"}}