{"slug": "guardrails-hub-guardrails-ai", "title": "Guardrails Hub – Guardrails AI", "summary": "Guardrails AI has launched the Guardrails Hub, a searchable repository of 70 validators designed to ensure the safety, quality, and compliance of AI-generated text. The hub includes validators for detecting PII, profanity, toxic language, and jailbreak attempts, as well as tools for factuality checks and translation quality, catering to developers integrating guardrails into their LLM applications.", "body_md": "Guardrails AI\nHub\nBlog\nDocs\nLoading ...\nThe Guardrails Hub\nSearch and explore vast world of guardrails validators through lightning-fast search\n70 validators\nValidators\n70 / 70\nArize Dataset Embeddings\nSelect\nValidates that user-generated input does not match the dataset of jailbreak embeddings from Arize AI.\nstring\nBrand risk\nML\nBan List\nSelect\nValidates that the output does not contain banned words, using fuzzy search.\nstring\nBrand risk\nML\nBespoke MiniCheck\nSelect\nValidates that the LLM-generated text is supported by the provided context using BespokeLabs.AI's MiniCheck API.\nstring\nBrand risk\n+1 more\nML\nBias Check\nSelect\nValidates that the text is free from biases related to age, gender, sex, ethnicity, religion, etc.\nstring\nBrand risk\nML\nCompetitor Check\nSelect\nFlags mentions of competitors. Fixes responses by filtering out competitor names.\nstring\nBrand risk\nML\nCorrect Language\nSelect\nValidate that an LLM-generated text is in the expected language. If the text is not in the expected language, the validator will attempt to translate it to the expected language.\nstring\nEtiquette\nML\nCucumber Expression Match\nSelect\nValidates that the input string matches a specified cucumber expression.\nstring\nBrand risk\nML\nDetect PII\nSelect\nDetects personally identifiable information (PII) in text, using Microsoft Presidio.\nstring\nData Leakage\nML\nExtracted Summary Sentences Match\nSelect\nThis validator checks if the extracted summary sentences match the original document.\nstring\nFactuality\n+1 more\nML\nExtractive Summary\nSelect\nUses fuzzy matching to detect if some text is a summary of a document.\nstring\nFactuality\nGibberish Text\nSelect\nA Guardrails AI validator to detect gibberish text.\nstring\nBrand risk\n+3 more\nML\nHigh Quality Translation\nSelect\nA validator that checks if a translation is of high quality.\nstring\nEtiquette\n+1 more\nML\nLlama Guard\nSelect\nA llama based validator which checks whether a given prompt is safe/unsafe by specifying a set of policies and lists the violating policies when applicable.\nstring\nEtiquette\nML\nLLM RAG Evaluator\nSelect\nThis validator uses an LLM Judge to decide whether the LLM response is acceptable in a RAG application.\nstring\nFactuality\n+1 more\nLLM\nLogic Check\nSelect\nValidates logical consistency and detects logical fallacies in the model output. Attempts to correct logical fallacies if found.\nstring\nBrand risk\nML\nNSFW Text\nSelect\nA Guardrails AI validator to detect NSFW text\nstring\nEtiquette\nML\nProfanity Free\nSelect\nChecks for profanity in text, using the alt-profanity-check library.\nstring\nEtiquette\n+1 more\nML\nProvenance Embeddings\nSelect\nCompares embeddings of generated and source texts to calculate provenance.\nstring\nFactuality\n+1 more\nML\nProvenance LLM\nSelect\nA validator for ensuring the factuality and reducing brand risk in generated content.\nstring\nFactuality\n+2 more\nML\nQA Relevance LLM Eval\nSelect\nMakes a second request to the LLM, asking it if its original response was relevant to the prompt.\nstring\nJailbreaking\n+1 more\nLLM\nRelevancy Evaluator\nSelect\nValidates that the reference text contains information relevant to answering the original question.\nstring\nBrand risk\nML\nRestrict to Topic\nSelect\nDetermines if the text pertains to a specified topic.\nstring\nEtiquette\n+3 more\nLLM\nSaliency Check\nSelect\nChecks if a generated summary covers topics present in a source document.\nstring\nFactuality\nLLM\nSecrets Present\nSelect\nDetects the secrets present in text by matching against common patterns for API keys and other sensitive information.\nstring\ncode\n+2 more\nRule\nShield Gemma\nSelect\nA Gemma based validator for moderating user prompts to guard against harmful content by specifying a policy.\nstring\nEtiquette\nML\nSimilar To Document\nSelect\nChecks if some generated text is similar to a provided document.\nstring\nFactuality\nML\nSimilar To Previous Values\nSelect\nChecks if a value is similar to a list of previously known correct values.\nstring\ninteger\n+1 more\nML\nToxic Language\nSelect\nIdentifies and flags toxic language in text to ensure communications remain professional and appropriate.\nstring\nEtiquette\nML\nWiki Provenance\nSelect\nA Guardrails AI validator that detects and removes hallucinated text based off Wikipedia\nstring\nFactuality\nContains String\nSelect\nA Guardrails AI validator to check if the LLM-generated text contains a substring.\nstring\nFormatting\nCSV Validator\nSelect\nChecks the CSV file for issues, including mismatched column lengths and inconsistent quote delimiters.\ncsv\nFormatting\n+1 more\nDetect Jailbreak\nSelect\nDetects attempts to circumvent safeguards in model conditioning.\nstring\nBrand risk\n+2 more\nML\nEndpoint Is Reachable\nSelect\nChecks if an endpoint can be reached by making a request to it.\nstring\nCode Exploits\n+1 more\nEnds With\nSelect\nCheck if a string or list ends with a specified string or list.\nlist\nstring\n+1 more\nExclude SQL Predicates\nSelect\nThis rule checks for the use of particular SQL predicates in the query. It is important to exclude SQL predicates from the query to prevent SQL injection attacks.\nstring\nsql\n+3 more\nFinancial Tone\nSelect\nValidates that an LLM-generated output (in a financial context) maintains a particular tone.\nstring\nEtiquette\nML\nGrounded AI Hallucination\nSelect\nA Grounded AI validator that detects hallucinated text\nstring\nFactuality\nGuardrails PII\nSelect\nDetects personally identifiable information (PII) in text.\nstring\nData Leakage\nML\nHas Url\nSelect\nEnsure content contains a url.\nstring\nCode Exploits\n+1 more\nLLM Critic\nSelect\nGrade the generated response based on provided criteria.\nstring\nFactuality\n+1 more\nLLM\nLowercase\nSelect\nPasses when totally lowercase.\nstring\nFormatting\nMentions Drugs\nSelect\nValidates that the generated text does not contain any drug names\nstring\nEtiquette\nML\nMLcube RAG Context Evaluator\nSelect\nA validator that scores retrieved RAG context for relevance and usefulness to the user query.\nstring\nFactuality\nOne Line\nSelect\nThis validator checks if the input is a single line of text.\nstring\nFormatting\nPoliteness Check\nSelect\nEnsure generated output is polite.\nstring\nEtiquette\nLLM\nPrompt Injection Detector\nSelect\nA Guardrails validator that scores prompts for injection attempts via a secondary LLM.\nstring\nJailbreaking\nLLM\nQuotes Price\nSelect\nValidates that the generated text contains a price quote\nstring\nBrand risk\nML\nReading Level\nSelect\nParses text to find its readability as a US grade level number (0-12).\nstring\nEtiquette\n+1 more\nReading Time\nSelect\nEnsures that any generated text is less than a maximum expected reading time.\nstring\nFormatting\nRedundant Sentences\nSelect\nIdentifies redundant sentences in text using fuzzy matching.\nstring\nEtiquette\nML\nRegex Match\nSelect\nEnsure content matches a provided regular expression. This can be used to validate content such as email addresses, phone numbers, and more.\nstring\nFormatting\nResponse Evaluator\nSelect\nEvaluate generated output using a provided question.\nstring\nFactuality\n+2 more\nLLM\nResponsiveness Check\nSelect\nEnsure generated output is polite.\nstring\nFactuality\nLLM\nSensitive Topic\nSelect\nA Guardrails AI validator that detects sensitive topics in text.\nstring\nEtiquette\nML\nSql Column Presence\nSelect\nChecks that schema columns are present in a SQL query.\nstring\nsql\n+3 more\nToxic Language LLM\nSelect\nDetects toxic language in LLM-generated text using an LLM as the detection backbone. Evaluates text across seven toxicity categories: toxicity, severe toxicity, obscene, threat, insult, identity attack, and sexual explicit content.\nstring\nEtiquette\nLLM\nTwo Words\nSelect\nPasses when value is *exactly* two words.\nstring\nFormatting\nUnusual Prompt\nSelect\nA Guardrails AI input validator that validates a prompt for unusualness and trickery.\nstring\nEtiquette\n+1 more\nLLM\nUppercase\nSelect\nPasses when totally uppercase.\nstring\nFormatting\nValid Address\nSelect\nVerifies an LLM-generated address using Google Maps' Address Validation API.\nstring\nFormatting\n+1 more\nValid Choices\nSelect\nChecks if a given string is a valid choice from a list of choices.\nstring\nFormatting\nValid HTML\nSelect\nGuardrails validator that checks for HTML parseability.\nstring\nFormatting\nValid JSON\nSelect\nEnsure content is parseable as valid JSON.\nstring\nobject\n+3 more\nValid Length\nSelect\nEnsures the length of a string or list falls between a minimum and maximum.\nstring\nFormatting\nValid OpenAPI Specification\nSelect\nEnsures that a generated output is a valid OpenAPI Specification.\nstring\nobject\n+2 more\nValid Python\nSelect\nValidates whether the given Python code is syntactically correct.\npython\nInvalid Code\nValid Range\nSelect\nAssess whether a generated number is between a maximum and minimum value.\ninteger\nfloat\n+1 more\nValid SQL\nSelect\nValidates whether the given SQL code is syntactically correct using. Optionally accepts a database schema to validate against using SQLAlchemy.\nsql\nInvalid Code\n+1 more\nValid URL\nSelect\nValidates that text is a syntactically-valid URL\nstring\nFormatting\nWeb Sanitization\nSelect\nScans LLM outputs for strings that could cause browser script execution downstream.\nstring\nCode Exploits\n0 Validators selected\nGenerate Code\nFilters\nUse Cases\nChatbots\nCustomer Support\nStructured data\nRAG\nSummarization\nCodeGen\nText2SQL\nRisk Category\nEtiquette\nBrand risk\nFactuality\nFormatting\nInvalid Code\nJailbreaking\nCode Exploits\nData Leakage\nInfrastructure Requirements\nML\nLLM\nNA\nRule\nContent Type\nstring\nobject\nlist\ninteger\nfloat\nsql\ncode\ncsv\npython\nCertification\nGuardrails Certified\nLanguage\nen\nThe Guardrails Hub\nSearch and explore vast world of guardrails validators through lightning-fast search\n70 validators\nValidators\n70 / 70\nArize Dataset Embeddings\nSelect\nValidates that user-generated input does not match the dataset of jailbreak embeddings from Arize AI.\nstring\nBrand risk\nML\nBan List\nSelect\nValidates that the output does not contain banned words, using fuzzy search.\nstring\nBrand risk\nML\nBespoke MiniCheck\nSelect\nValidates that the LLM-generated text is supported by the provided context using BespokeLabs.AI's MiniCheck API.\nstring\nBrand risk\n+1 more\nML\nBias Check\nSelect\nValidates that the text is free from biases related to age, gender, sex, ethnicity, religion, etc.\nstring\nBrand risk\nML\nCompetitor Check\nSelect\nFlags mentions of competitors. Fixes responses by filtering out competitor names.\nstring\nBrand risk\nML\nCorrect Language\nSelect\nValidate that an LLM-generated text is in the expected language. If the text is not in the expected language, the validator will attempt to translate it to the expected language.\nstring\nEtiquette\nML\nCucumber Expression Match\nSelect\nValidates that the input string matches a specified cucumber expression.\nstring\nBrand risk\nML\nDetect PII\nSelect\nDetects personally identifiable information (PII) in text, using Microsoft Presidio.\nstring\nData Leakage\nML\nExtracted Summary Sentences Match\nSelect\nThis validator checks if the extracted summary sentences match the original document.\nstring\nFactuality\n+1 more\nML\nExtractive Summary\nSelect\nUses fuzzy matching to detect if some text is a summary of a document.\nstring\nFactuality\nGibberish Text\nSelect\nA Guardrails AI validator to detect gibberish text.\nstring\nBrand risk\n+3 more\nML\nHigh Quality Translation\nSelect\nA validator that checks if a translation is of high quality.\nstring\nEtiquette\n+1 more\nML\nLlama Guard\nSelect\nA llama based validator which checks whether a given prompt is safe/unsafe by specifying a set of policies and lists the violating policies when applicable.\nstring\nEtiquette\nML\nLLM RAG Evaluator\nSelect\nThis validator uses an LLM Judge to decide whether the LLM response is acceptable in a RAG application.\nstring\nFactuality\n+1 more\nLLM\nLogic Check\nSelect\nValidates logical consistency and detects logical fallacies in the model output. Attempts to correct logical fallacies if found.\nstring\nBrand risk\nML\nNSFW Text\nSelect\nA Guardrails AI validator to detect NSFW text\nstring\nEtiquette\nML\nProfanity Free\nSelect\nChecks for profanity in text, using the alt-profanity-check library.\nstring\nEtiquette\n+1 more\nML\nProvenance Embeddings\nSelect\nCompares embeddings of generated and source texts to calculate provenance.\nstring\nFactuality\n+1 more\nML\nProvenance LLM\nSelect\nA validator for ensuring the factuality and reducing brand risk in generated content.\nstring\nFactuality\n+2 more\nML\nQA Relevance LLM Eval\nSelect\nMakes a second request to the LLM, asking it if its original response was relevant to the prompt.\nstring\nJailbreaking\n+1 more\nLLM\nRelevancy Evaluator\nSelect\nValidates that the reference text contains information relevant to answering the original question.\nstring\nBrand risk\nML\nRestrict to Topic\nSelect\nDetermines if the text pertains to a specified topic.\nstring\nEtiquette\n+3 more\nLLM\nSaliency Check\nSelect\nChecks if a generated summary covers topics present in a source document.\nstring\nFactuality\nLLM\nSecrets Present\nSelect\nDetects the secrets present in text by matching against common patterns for API keys and other sensitive information.\nstring\ncode\n+2 more\nRule\nShield Gemma\nSelect\nA Gemma based validator for moderating user prompts to guard against harmful content by specifying a policy.\nstring\nEtiquette\nML\nSimilar To Document\nSelect\nChecks if some generated text is similar to a provided document.\nstring\nFactuality\nML\nSimilar To Previous Values\nSelect\nChecks if a value is similar to a list of previously known correct values.\nstring\ninteger\n+1 more\nML\nToxic Language\nSelect\nIdentifies and flags toxic language in text to ensure communications remain professional and appropriate.\nstring\nEtiquette\nML\nWiki Provenance\nSelect\nA Guardrails AI validator that detects and removes hallucinated text based off Wikipedia\nstring\nFactuality\nContains String\nSelect\nA Guardrails AI validator to check if the LLM-generated text contains a substring.\nstring\nFormatting\nCSV Validator\nSelect\nChecks the CSV file for issues, including mismatched column lengths and inconsistent quote delimiters.\ncsv\nFormatting\n+1 more\nDetect Jailbreak\nSelect\nDetects attempts to circumvent safeguards in model conditioning.\nstring\nBrand risk\n+2 more\nML\nEndpoint Is Reachable\nSelect\nChecks if an endpoint can be reached by making a request to it.\nstring\nCode Exploits\n+1 more\nEnds With\nSelect\nCheck if a string or list ends with a specified string or list.\nlist\nstring\n+1 more\nExclude SQL Predicates\nSelect\nThis rule checks for the use of particular SQL predicates in the query. It is important to exclude SQL predicates from the query to prevent SQL injection attacks.\nstring\nsql\n+3 more\nFinancial Tone\nSelect\nValidates that an LLM-generated output (in a financial context) maintains a particular tone.\nstring\nEtiquette\nML\nGrounded AI Hallucination\nSelect\nA Grounded AI validator that detects hallucinated text\nstring\nFactuality\nGuardrails PII\nSelect\nDetects personally identifiable information (PII) in text.\nstring\nData Leakage\nML\nHas Url\nSelect\nEnsure content contains a url.\nstring\nCode Exploits\n+1 more\nLLM Critic\nSelect\nGrade the generated response based on provided criteria.\nstring\nFactuality\n+1 more\nLLM\nLowercase\nSelect\nPasses when totally lowercase.\nstring\nFormatting\nMentions Drugs\nSelect\nValidates that the generated text does not contain any drug names\nstring\nEtiquette\nML\nMLcube RAG Context Evaluator\nSelect\nA validator that scores retrieved RAG context for relevance and usefulness to the user query.\nstring\nFactuality\nOne Line\nSelect\nThis validator checks if the input is a single line of text.\nstring\nFormatting\nPoliteness Check\nSelect\nEnsure generated output is polite.\nstring\nEtiquette\nLLM\nPrompt Injection Detector\nSelect\nA Guardrails validator that scores prompts for injection attempts via a secondary LLM.\nstring\nJailbreaking\nLLM\nQuotes Price\nSelect\nValidates that the generated text contains a price quote\nstring\nBrand risk\nML\nReading Level\nSelect\nParses text to find its readability as a US grade level number (0-12).\nstring\nEtiquette\n+1 more\nReading Time\nSelect\nEnsures that any generated text is less than a maximum expected reading time.\nstring\nFormatting\nRedundant Sentences\nSelect\nIdentifies redundant sentences in text using fuzzy matching.\nstring\nEtiquette\nML\nRegex Match\nSelect\nEnsure content matches a provided regular expression. This can be used to validate content such as email addresses, phone numbers, and more.\nstring\nFormatting\nResponse Evaluator\nSelect\nEvaluate generated output using a provided question.\nstring\nFactuality\n+2 more\nLLM\nResponsiveness Check\nSelect\nEnsure generated output is polite.\nstring\nFactuality\nLLM\nSensitive Topic\nSelect\nA Guardrails AI validator that detects sensitive topics in text.\nstring\nEtiquette\nML\nSql Column Presence\nSelect\nChecks that schema columns are present in a SQL query.\nstring\nsql\n+3 more\nToxic Language LLM\nSelect\nDetects toxic language in LLM-generated text using an LLM as the detection backbone. Evaluates text across seven toxicity categories: toxicity, severe toxicity, obscene, threat, insult, identity attack, and sexual explicit content.\nstring\nEtiquette\nLLM\nTwo Words\nSelect\nPasses when value is *exactly* two words.\nstring\nFormatting\nUnusual Prompt\nSelect\nA Guardrails AI input validator that validates a prompt for unusualness and trickery.\nstring\nEtiquette\n+1 more\nLLM\nUppercase\nSelect\nPasses when totally uppercase.\nstring\nFormatting\nValid Address\nSelect\nVerifies an LLM-generated address using Google Maps' Address Validation API.\nstring\nFormatting\n+1 more\nValid Choices\nSelect\nChecks if a given string is a valid choice from a list of choices.\nstring\nFormatting\nValid HTML\nSelect\nGuardrails validator that checks for HTML parseability.\nstring\nFormatting\nValid JSON\nSelect\nEnsure content is parseable as valid JSON.\nstring\nobject\n+3 more\nValid Length\nSelect\nEnsures the length of a string or list falls between a minimum and maximum.\nstring\nFormatting\nValid OpenAPI Specification\nSelect\nEnsures that a generated output is a valid OpenAPI Specification.\nstring\nobject\n+2 more\nValid Python\nSelect\nValidates whether the given Python code is syntactically correct.\npython\nInvalid Code\nValid Range\nSelect\nAssess whether a generated number is between a maximum and minimum value.\ninteger\nfloat\n+1 more\nValid SQL\nSelect\nValidates whether the given SQL code is syntactically correct using. Optionally accepts a database schema to validate against using SQLAlchemy.\nsql\nInvalid Code\n+1 more\nValid URL\nSelect\nValidates that text is a syntactically-valid URL\nstring\nFormatting\nWeb Sanitization\nSelect\nScans LLM outputs for strings that could cause browser script execution downstream.\nstring\nCode Exploits\n0 Validators selected\nGenerate Code", "url": "https://wpnews.pro/news/guardrails-hub-guardrails-ai", "canonical_source": "https://guardrailsai.com/hub", "published_at": "2026-07-31 12:27:34+00:00", "updated_at": "2026-07-31 12:53:25.198944+00:00", "lang": "en", "topics": ["ai-safety", "ai-tools", "ai-products", "natural-language-processing"], "entities": ["Guardrails AI", "Arize AI", "BespokeLabs.AI", "Microsoft Presidio"], "alternates": {"html": "https://wpnews.pro/news/guardrails-hub-guardrails-ai", "markdown": "https://wpnews.pro/news/guardrails-hub-guardrails-ai.md", "text": "https://wpnews.pro/news/guardrails-hub-guardrails-ai.txt", "jsonld": "https://wpnews.pro/news/guardrails-hub-guardrails-ai.jsonld"}}