{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"llm-guard","slug":"llm-guard","name":"LLM Guard","type":"framework","url":"https://github.com/protectai/llm-guard","page_url":"https://unfragile.ai/llm-guard","categories":["code-review-security"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"llm-guard__cap_0","uri":"capability://safety.moderation.dual.gate.prompt.and.output.scanning.with.unified.scanner.interface","name":"dual-gate prompt and output scanning with unified scanner interface","description":"Implements a modular scanner framework where both input (pre-LLM) and output (post-LLM) validators follow a common interface returning (sanitized_text, is_valid, risk_score) tuples. Scanners are composed independently and can be chained in arbitrary order, enabling flexible security pipelines. The architecture decouples scanner logic from orchestration, allowing developers to enable/disable scanners via configuration without code changes.","intents":["I want to validate user prompts before sending them to an LLM to prevent injection attacks","I need to scan LLM responses before returning them to users to filter harmful content","I want to compose multiple security checks in a pipeline without writing custom orchestration code","I need to enable/disable specific security scanners based on deployment environment or risk profile"],"best_for":["teams building LLM applications requiring defense-in-depth security","developers integrating LLM Guard into existing LLM pipelines (LiteLLM, LangChain)","security-conscious organizations needing configurable, auditable scanning policies"],"limitations":["Scanner composition adds latency per chain step (no batching optimization across scanners)","Risk scores are scanner-specific and not normalized across different scanner types","No built-in persistence for scan results — requires external logging/monitoring integration","Scanners execute sequentially; no parallel execution optimization for independent scanners"],"requires":["Python 3.9+","PyPI package: llm-guard","For model-based scanners: HuggingFace transformers library and model weights (auto-downloaded on first use)"],"input_types":["text (user prompts, LLM outputs)","structured metadata (optional context for some scanners)"],"output_types":["sanitized text (with redactions/replacements applied)","boolean validity flag","float risk score (0.0-1.0)"],"categories":["safety-moderation","security-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_1","uri":"capability://safety.moderation.prompt.injection.detection.via.multiple.pattern.and.semantic.approaches","name":"prompt injection detection via multiple pattern and semantic approaches","description":"Detects prompt injection attacks using a multi-strategy approach combining regex-based pattern matching for known injection signatures, semantic similarity analysis against injection templates, and structural analysis of prompt delimiters and role-switching patterns. The scanner identifies attempts to override system instructions, inject new directives, or manipulate LLM behavior through adversarial prompt crafting.","intents":["I want to detect when users are trying to override my LLM's system instructions or inject malicious prompts","I need to identify prompt injection attempts that use delimiter manipulation or role-switching tactics","I want to block prompts that attempt to exfiltrate system prompts or model weights through injection"],"best_for":["developers building user-facing LLM chatbots or assistants","teams deploying LLMs in multi-tenant environments where prompt injection is a primary threat","security teams needing to audit and log injection attempts for compliance"],"limitations":["Pattern-based detection can be evaded by obfuscation or novel injection techniques not in signature database","Semantic detection requires embedding models which add ~50-200ms latency per scan","False positives possible for legitimate prompts containing injection-like keywords (e.g., 'ignore previous instructions' in educational contexts)","Cannot detect sophisticated multi-turn injection attacks that span multiple conversation turns"],"requires":["Python 3.9+","llm-guard library with prompt_injection scanner enabled","For semantic detection: HuggingFace sentence-transformers model (auto-downloaded, ~400MB)"],"input_types":["text (user prompt)"],"output_types":["sanitized prompt (with injection markers removed or flagged)","boolean is_valid flag","float risk_score indicating injection likelihood (0.0-1.0)"],"categories":["safety-moderation","security-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_10","uri":"capability://data.processing.analysis.onnx.model.optimization.for.low.latency.and.resource.constrained.deployment","name":"onnx model optimization for low-latency and resource-constrained deployment","description":"Supports ONNX (Open Neural Network Exchange) optimization for transformer-based scanners, enabling faster inference and reduced memory footprint. Converts HuggingFace models to ONNX format with quantization options (int8, float16), enabling deployment on CPU-only or edge devices. Configuration-driven ONNX enablement allows switching between full-precision and optimized models without code changes. Reduces model inference latency by 2-10x compared to PyTorch, enabling real-time scanning in latency-sensitive applications.","intents":["I want to deploy LLM Guard scanners on CPU-only servers without GPU","I need to reduce scanning latency to <50ms per request for real-time applications","I want to deploy scanners on edge devices or serverless functions with limited memory","I need to optimize model inference cost by reducing computational requirements"],"best_for":["teams deploying on cost-constrained infrastructure (CPU-only servers, edge devices)","applications with strict latency requirements (<100ms per request)","serverless/FaaS deployments where cold start time and memory are critical","resource-constrained environments (mobile, embedded systems)"],"limitations":["ONNX conversion requires additional setup and model compilation step","Quantization (int8, float16) may reduce model accuracy by 1-5% depending on model and quantization level","Not all scanner types support ONNX optimization; only transformer-based models benefit","ONNX Runtime compatibility varies by platform; may require platform-specific builds"],"requires":["Python 3.9+","llm-guard library with ONNX support enabled","ONNX Runtime library (pip install onnxruntime)","Optional: ONNX conversion tools (optimum library) for custom model optimization","Configuration file specifying ONNX model paths and quantization settings"],"input_types":["text (user prompt or LLM output)"],"output_types":["same as non-optimized scanners (sanitized_text, is_valid, risk_score)"],"categories":["data-processing-analysis","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_11","uri":"capability://automation.workflow.configurable.scanner.composition.and.policy.driven.security.pipelines","name":"configurable scanner composition and policy-driven security pipelines","description":"Enables developers to compose scanners into custom security pipelines via configuration files (YAML) or code, selecting which scanners to enable, their order, and their parameters. Supports conditional scanner execution (e.g., run PII scanner only if prompt contains certain keywords), scanner chaining (output of one scanner feeds into next), and policy-driven behavior (different scanner sets for different user roles or risk profiles). Eliminates need to write custom orchestration code for complex security workflows.","intents":["I want to enable/disable specific scanners based on my security requirements without code changes","I need different scanning policies for different user types (e.g., stricter for anonymous users)","I want to compose scanners in specific order to optimize latency or accuracy","I need to implement conditional scanning logic (e.g., run expensive scanners only when needed)"],"best_for":["teams with complex security policies requiring flexible scanner composition","organizations with multiple deployment environments needing different scanning policies","developers wanting to avoid writing custom scanner orchestration code"],"limitations":["Configuration complexity increases with number of different policies needed","Conditional logic in configuration files can become hard to maintain for complex scenarios","No built-in versioning or rollback for configuration changes; requires external version control","Performance impact of conditional logic and scanner chaining not well-documented"],"requires":["Python 3.9+","llm-guard library","YAML configuration file (for API service) or Python code (for library usage)"],"input_types":["YAML configuration file or Python code defining scanner composition"],"output_types":["configured scanner pipeline ready for execution"],"categories":["automation-workflow","policy-enforcement"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_12","uri":"capability://automation.workflow.observability.and.audit.logging.for.security.scanning.decisions","name":"observability and audit logging for security scanning decisions","description":"Provides hooks for logging and monitoring all scanning decisions, enabling compliance auditing and security analysis. Integrates with standard Python logging framework and supports custom observability backends. Logs include scanner name, input text, risk score, sanitization actions, and decision (allow/block). Enables teams to audit security decisions, identify patterns in attacks, and monitor scanner performance. Supports structured logging (JSON) for integration with log aggregation systems (ELK, Datadog, Splunk).","intents":["I want to audit all security scanning decisions for compliance and forensics","I need to monitor scanner performance and identify bottlenecks","I want to detect patterns in attack attempts and adjust policies accordingly","I need to integrate scanning logs with my SIEM or log aggregation system"],"best_for":["security-conscious organizations requiring audit trails for compliance","teams needing to monitor and analyze attack patterns","organizations with SIEM/log aggregation infrastructure"],"limitations":["Logging adds overhead (~5-20ms per scan depending on log destination)","Logging sensitive data (original prompts, PII) creates compliance risks; requires careful log filtering","No built-in log retention or archival; requires external log storage","Structured logging format is custom; requires custom parsing for integration with some log systems"],"requires":["Python 3.9+","llm-guard library","Python logging configuration","Optional: log aggregation system (ELK, Datadog, Splunk) for centralized logging"],"input_types":["scanning results (scanner name, risk score, decision)"],"output_types":["structured log entries (JSON or text) with scanning details"],"categories":["automation-workflow","monitoring-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_13","uri":"capability://data.processing.analysis.batch.scanning.with.multi.text.processing","name":"batch scanning with multi-text processing","description":"Supports scanning multiple prompts or outputs in a single API call, enabling efficient batch processing for high-throughput scenarios. Processes batches through the scanner pipeline with optimized tensor operations and optional parallelization, reducing per-item overhead compared to individual requests.","intents":["I need to scan large volumes of text efficiently (e.g., processing historical chat logs)","I want to reduce API call overhead by batching multiple prompts together","I need to process datasets of prompts/outputs for security auditing"],"best_for":["teams processing large datasets of LLM interactions for security auditing","batch processing pipelines that scan historical data","organizations optimizing API costs by reducing request overhead"],"limitations":["Batch processing requires buffering multiple texts in memory — can cause OOM errors for very large batches","Optimal batch size depends on model and hardware — requires tuning for each deployment","No built-in request queuing — batches must be assembled by the caller","Batch results are returned together — no streaming results for real-time applications"],"requires":["Python 3.9+","Sufficient memory to hold batch of texts and model outputs"],"input_types":["list of text strings (prompts or outputs)"],"output_types":["list of scanner results (one per input text)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_14","uri":"capability://planning.reasoning.risk.score.aggregation.and.policy.based.decision.making","name":"risk score aggregation and policy-based decision making","description":"Aggregates risk scores from multiple scanners using configurable strategies (weighted sum, maximum, AND/OR logic) to produce a final security decision. Enables policy-based rules (e.g., 'block if any scanner scores > 0.8 OR toxicity > 0.9') for nuanced security decisions beyond binary allow/block.","intents":["I need to combine results from multiple scanners into a single security decision","I want to define policies that weight different security concerns differently (e.g., PII is critical, toxicity is moderate)","I need to allow some risk while blocking high-risk content"],"best_for":["security teams implementing nuanced content policies","organizations with different risk tolerances for different content types","developers building adaptive security that adjusts based on context"],"limitations":["Risk score aggregation requires manual tuning of weights and thresholds — no automated calibration","Different scanners produce scores on different scales — normalization is required but not automatic","Policy rules can become complex and hard to understand — no visual policy editor","No built-in A/B testing for policy changes — requires manual testing before deployment"],"requires":["Python 3.9+","Configuration of aggregation strategy and policy rules"],"input_types":["risk scores from multiple scanners"],"output_types":["aggregated risk score (0-1)","boolean decision (allow/block)","policy rule that triggered decision"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_2","uri":"capability://safety.moderation.pii.detection.and.anonymization.with.stateful.vault.storage","name":"pii detection and anonymization with stateful vault storage","description":"Detects personally identifiable information (names, emails, phone numbers, SSNs, credit cards, etc.) in prompts and outputs using pattern matching and NER (Named Entity Recognition) models. Detected PII can be anonymized by replacing with tokens and storing original values in a stateful Vault object, enabling later de-anonymization. The Vault class maintains in-memory or persistent storage of PII mappings, supporting workflows where sensitive data must be redacted from LLM context but recovered in responses.","intents":["I want to detect and redact PII from user prompts before sending to LLM to prevent data leakage","I need to anonymize sensitive data in LLM outputs while preserving the ability to restore original values","I want to prevent the LLM from learning or memorizing user PII through training or context windows","I need to comply with data protection regulations (GDPR, CCPA) by ensuring PII doesn't reach third-party LLM APIs"],"best_for":["healthcare and financial services teams handling sensitive customer data","developers building LLM applications in regulated industries (HIPAA, PCI-DSS compliance)","teams using third-party LLM APIs and needing to redact PII before transmission"],"limitations":["NER models have ~85-95% accuracy; some PII types (context-dependent sensitive data) may be missed","Vault storage is in-memory by default; requires external persistence layer for production use (database, encrypted file storage)","De-anonymization requires maintaining Vault state across requests; stateless deployments need external state management","Pattern-based detection (regex) for structured PII (SSN, credit card) can have false positives/negatives depending on context"],"requires":["Python 3.9+","llm-guard library with pii_scanner enabled","For NER-based detection: HuggingFace transformers with NER model (e.g., dslim/bert-base-NER, ~400MB)","Optional: external state store (Redis, PostgreSQL) for persistent Vault storage in distributed deployments"],"input_types":["text (user prompt or LLM output)"],"output_types":["anonymized text (with PII replaced by tokens like [PERSON_1], [EMAIL_1])","boolean is_valid flag","float risk_score (proportion of text identified as PII)","Vault object with PII mappings for de-anonymization"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_3","uri":"capability://safety.moderation.toxic.content.and.harmful.language.detection.with.configurable.severity.thresholds","name":"toxic content and harmful language detection with configurable severity thresholds","description":"Detects toxic, abusive, and harmful language in prompts and outputs using transformer-based text classification models trained on toxicity datasets. Scanners classify text into categories (profanity, insults, threats, harassment) and assign risk scores. Developers can configure severity thresholds to reject or flag content based on risk tolerance, enabling fine-grained control over what language is permitted in different contexts.","intents":["I want to filter out profanity and abusive language from user prompts to maintain a safe environment","I need to detect and block toxic LLM outputs before they reach users","I want to allow some mild language but block severe threats or harassment","I need to log and monitor toxic content for moderation workflows"],"best_for":["community platforms and social applications using LLMs for content generation","customer service chatbots needing to maintain professional tone","educational platforms requiring safe LLM interactions for minors"],"limitations":["Toxicity models are trained on English datasets; performance degrades significantly for non-English languages","Context-dependent toxicity (e.g., reclaimed slurs, academic discussion of harmful topics) may be misclassified","Model accuracy ~85-90%; false positives possible for sarcasm, quotes, or educational content about harmful topics","Adds ~100-300ms latency per scan due to transformer model inference"],"requires":["Python 3.9+","llm-guard library with toxicity scanner enabled","HuggingFace transformers with toxicity classification model (e.g., michellejieli/NSFW_text_classifier, ~500MB)"],"input_types":["text (user prompt or LLM output)"],"output_types":["sanitized text (optionally with toxic phrases redacted)","boolean is_valid flag","float risk_score (0.0-1.0 toxicity probability)","optional: toxicity category labels (profanity, insult, threat, harassment)"],"categories":["safety-moderation","content-filtering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_4","uri":"capability://safety.moderation.sensitive.topic.and.banned.content.filtering.with.custom.policy.configuration","name":"sensitive topic and banned content filtering with custom policy configuration","description":"Detects and filters prompts/outputs containing banned topics or sensitive subjects (e.g., violence, self-harm, illegal activities, adult content) based on configurable policy lists. Uses semantic similarity matching against topic keywords and phrases to identify content violating organizational policies. Developers can define custom banned topic lists per deployment, enabling different policies for different user segments or jurisdictions.","intents":["I want to prevent users from asking the LLM to help with illegal activities or violence","I need to block LLM outputs discussing self-harm or suicide to protect vulnerable users","I want to enforce different content policies for different user groups (e.g., stricter for minors)","I need to comply with regional content regulations (e.g., GDPR restrictions on certain topics)"],"best_for":["platforms serving minors or vulnerable populations requiring strict content policies","organizations with compliance requirements restricting certain topics (healthcare, finance, government)","multi-tenant SaaS platforms needing per-customer content policies"],"limitations":["Semantic similarity matching requires embedding models; adds ~50-150ms latency per scan","False positives possible for legitimate discussion of sensitive topics (e.g., educational content about violence)","Custom policy lists require manual curation; no automated policy generation or learning from moderation decisions","Topic detection is language-specific; multilingual deployments require separate models per language"],"requires":["Python 3.9+","llm-guard library with banned_topics scanner enabled","HuggingFace sentence-transformers for semantic similarity (auto-downloaded, ~400MB)","Configuration file or code defining banned topic keywords/phrases"],"input_types":["text (user prompt or LLM output)"],"output_types":["sanitized text (with banned topic references removed or redacted)","boolean is_valid flag","float risk_score (semantic similarity to banned topics)","optional: matched banned topic category"],"categories":["safety-moderation","policy-enforcement"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_5","uri":"capability://safety.moderation.code.injection.and.malicious.code.detection.in.prompts.and.outputs","name":"code injection and malicious code detection in prompts and outputs","description":"Detects attempts to inject executable code (SQL, shell commands, Python, JavaScript) into prompts or malicious code in LLM outputs. Uses pattern matching for common injection signatures (SQL keywords, shell metacharacters), AST parsing for code structure analysis, and optional semantic analysis to identify code-like patterns. Prevents LLM from being used to generate or execute malicious code, and blocks prompts attempting to manipulate backend systems through code injection.","intents":["I want to prevent users from injecting SQL or shell commands into prompts to attack backend systems","I need to detect when LLM outputs contain executable code that could be malicious if run","I want to block prompts attempting to trick the LLM into generating code for hacking or exploitation","I need to prevent code injection attacks in applications that execute LLM-generated code"],"best_for":["applications that execute LLM-generated code (code generation tools, automation platforms)","systems accepting user prompts that interact with databases or shell environments","security-critical applications where code injection is a primary threat vector"],"limitations":["Pattern-based detection can be evaded by obfuscation, encoding, or novel injection techniques","AST parsing requires language-specific parsers; only detects code in supported languages (Python, JavaScript, SQL, Bash)","High false positive rate for legitimate code discussions or examples in prompts","Cannot detect sophisticated multi-stage injection attacks or code generation that only becomes malicious after execution"],"requires":["Python 3.9+","llm-guard library with code_injection scanner enabled","Optional: language-specific parsers (ast module for Python, tree-sitter for other languages)"],"input_types":["text (user prompt or LLM output)"],"output_types":["sanitized text (with code snippets removed or redacted)","boolean is_valid flag","float risk_score (likelihood of malicious code)","optional: detected code language and injection type"],"categories":["safety-moderation","code-security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_6","uri":"capability://safety.moderation.invisible.unicode.and.encoding.based.obfuscation.detection","name":"invisible unicode and encoding-based obfuscation detection","description":"Detects attempts to hide malicious content using invisible unicode characters, zero-width characters, homoglyph attacks, and other encoding-based obfuscation techniques. Analyzes character encodings to identify non-printable characters, combining marks, and lookalike characters that could bypass other security scanners or confuse users. Prevents attackers from using unicode tricks to inject prompts or hide malicious instructions in LLM outputs.","intents":["I want to detect when users are using invisible characters to hide injection attacks or bypass my scanners","I need to identify homoglyph attacks where lookalike characters are used to impersonate legitimate content","I want to block prompts using zero-width characters or combining marks to obfuscate malicious instructions","I need to prevent unicode-based evasion techniques in adversarial prompt attacks"],"best_for":["security-conscious teams defending against sophisticated adversarial attacks","systems where unicode obfuscation is a known attack vector","platforms requiring defense against unicode-based prompt injection variants"],"limitations":["Legitimate use of combining marks and diacritics (e.g., accented characters) may be flagged as suspicious","Different unicode normalization forms (NFC, NFD, NFKC, NFKD) can affect detection; requires careful handling","False positives for non-Latin scripts with legitimate use of zero-width joiners or other control characters","Detection is heuristic-based; sophisticated obfuscation techniques may evade detection"],"requires":["Python 3.9+","llm-guard library with invisible_characters scanner enabled","No external dependencies; uses Python's built-in unicodedata module"],"input_types":["text (user prompt or LLM output)"],"output_types":["sanitized text (with invisible characters removed or replaced)","boolean is_valid flag","float risk_score (proportion of text containing suspicious unicode)","optional: list of detected obfuscation techniques"],"categories":["safety-moderation","encoding-security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_7","uri":"capability://data.processing.analysis.token.length.validation.and.context.window.management","name":"token length validation and context window management","description":"Validates that prompts and outputs fit within LLM context windows by tokenizing text using language-specific tokenizers (HuggingFace, OpenAI, Anthropic). Calculates token counts for prompts and outputs, enforces maximum token limits, and provides warnings when approaching context window limits. Integrates with multiple tokenizer backends, enabling accurate token counting for different LLM providers without sending data to external APIs.","intents":["I want to reject prompts that are too long to fit in the LLM's context window","I need to ensure LLM outputs don't exceed token limits before returning to users","I want to track token usage for billing and quota management","I need to validate token counts accurately for different LLM models (GPT-4, Claude, Llama) without API calls"],"best_for":["developers building LLM applications with strict token budgets","platforms charging users per token and needing accurate token counting","teams using multiple LLM providers with different context window sizes"],"limitations":["Tokenizer accuracy varies by model; different tokenizers may produce different token counts for same text","Requires downloading and maintaining tokenizer models for each LLM provider","Token counting is approximate for some models; actual token count may differ slightly from calculated count","No optimization for token efficiency; scanner only validates limits, doesn't compress or optimize prompts"],"requires":["Python 3.9+","llm-guard library with token_limit scanner enabled","HuggingFace transformers library","Tokenizer models for target LLM providers (auto-downloaded on first use)"],"input_types":["text (user prompt or LLM output)"],"output_types":["text (unchanged)","boolean is_valid flag (true if token count within limit)","integer token_count (calculated token count)","optional: token_limit (configured maximum)"],"categories":["data-processing-analysis","resource-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_8","uri":"capability://tool.use.integration.rest.api.service.for.remote.scanner.deployment.and.orchestration","name":"rest api service for remote scanner deployment and orchestration","description":"Exposes LLM Guard scanners via FastAPI HTTP endpoints, enabling remote deployment of security scanning as a microservice. The API service (llm-guard-api) wraps scanner implementations with REST endpoints for prompt validation, output validation, and batch scanning. Supports configuration-driven scanner selection via YAML, Docker deployment with GPU acceleration, and observability hooks for logging and monitoring. Enables teams to deploy scanning infrastructure separately from application code.","intents":["I want to deploy LLM Guard as a separate microservice that my applications call via HTTP","I need to scale scanning independently from my LLM application","I want to use GPU acceleration for model-based scanners in a containerized environment","I need to monitor and log all scanning decisions for compliance and debugging"],"best_for":["teams with microservices architectures wanting to centralize security scanning","organizations deploying LLM applications across multiple services/languages","teams needing GPU-accelerated scanning for high-throughput environments"],"limitations":["Network latency added by HTTP calls (~10-50ms per request) compared to in-process library usage","API service requires separate deployment, monitoring, and scaling infrastructure","Stateful operations (Vault for PII storage) require external state management (Redis, database)","No built-in authentication/authorization; requires external API gateway or authentication layer"],"requires":["Python 3.9+","FastAPI and uvicorn for API service","Docker for containerized deployment","Optional: CUDA toolkit for GPU acceleration (Dockerfile-cuda provided)","Configuration file (scanners.yml) defining enabled scanners and parameters"],"input_types":["JSON request body with text field (prompt or output to scan)"],"output_types":["JSON response with sanitized_text, is_valid, risk_score, and per-scanner results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__cap_9","uri":"capability://tool.use.integration.litellm.integration.for.transparent.scanner.injection.into.llm.calls","name":"litellm integration for transparent scanner injection into llm calls","description":"Provides native integration with LiteLLM proxy, enabling automatic injection of LLM Guard scanners into LLM API calls without modifying application code. Scanners run transparently before prompts reach the LLM and after responses are generated, implementing the dual-gate security model. Configuration-driven scanner selection allows different scanning policies per model, provider, or user without code changes. Supports all LiteLLM-compatible providers (OpenAI, Anthropic, Ollama, etc.).","intents":["I want to add security scanning to my LiteLLM calls without rewriting my application code","I need to apply different scanning policies to different LLM providers or models","I want to transparently scan all LLM interactions in my application with minimal integration effort","I need to audit and log all LLM interactions with security scanning results"],"best_for":["teams already using LiteLLM for multi-provider LLM abstraction","developers wanting to add security scanning with minimal code changes","organizations needing transparent security enforcement across multiple LLM providers"],"limitations":["Requires LiteLLM as dependency; adds complexity if not already in use","Integration is LiteLLM-specific; not compatible with direct OpenAI/Anthropic SDK calls","Scanning latency is added to every LLM call; no option to bypass scanning for trusted prompts","Configuration complexity increases with number of different scanning policies needed"],"requires":["Python 3.9+","LiteLLM library (pip install litellm)","llm-guard library with LiteLLM integration enabled","LiteLLM configuration with LLM Guard middleware enabled"],"input_types":["LiteLLM completion() or chat.completions.create() calls with messages parameter"],"output_types":["LiteLLM response object (unchanged structure, but with scanning applied transparently)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llm-guard__headline","uri":"capability://safety.moderation.llm.security.toolkit","name":"llm security toolkit","description":"An open-source framework designed to secure interactions with Large Language Models (LLMs) by detecting prompt injections, toxicity, and sensitive data across multiple scanner types.","intents":["best LLM security toolkit","LLM protection for prompt injection","open-source LLM security solutions","how to secure LLM outputs","tools for LLM content filtering","best practices for LLM security"],"best_for":["developers working with LLMs","organizations needing to secure AI interactions"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PyPI package: llm-guard","For model-based scanners: HuggingFace transformers library and model weights (auto-downloaded on first use)","llm-guard library with prompt_injection scanner enabled","For semantic detection: HuggingFace sentence-transformers model (auto-downloaded, ~400MB)","llm-guard library with ONNX support enabled","ONNX Runtime library (pip install onnxruntime)","Optional: ONNX conversion tools (optimum library) for custom model optimization","Configuration file specifying ONNX model paths and quantization settings","llm-guard library"],"failure_modes":["Scanner composition adds latency per chain step (no batching optimization across scanners)","Risk scores are scanner-specific and not normalized across different scanner types","No built-in persistence for scan results — requires external logging/monitoring integration","Scanners execute sequentially; no parallel execution optimization for independent scanners","Pattern-based detection can be evaded by obfuscation or novel injection techniques not in signature database","Semantic detection requires embedding models which add ~50-200ms latency per scan","False positives possible for legitimate prompts containing injection-like keywords (e.g., 'ignore previous instructions' in educational contexts)","Cannot detect sophisticated multi-turn injection attacks that span multiple conversation turns","ONNX conversion requires additional setup and model compilation step","Quantization (int8, float16) may reduce model accuracy by 1-5% depending on model and quantization level","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":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=llm-guard","compare_url":"https://unfragile.ai/compare?artifact=llm-guard"}},"signature":"9gDwrWmBhzNL6BUIeGxFk+GX4HLi8mT3cR29iuDiA2De1M/vUferFlI92z1RekjAXzv5DqCMLbsjZenfcN1tCQ==","signedAt":"2026-06-21T07:52:39.664Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm-guard","artifact":"https://unfragile.ai/llm-guard","verify":"https://unfragile.ai/api/v1/verify?slug=llm-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"}}