import logging from typing import Dict, List import streamlit as st from streamlit_tags import st_tags from llm_guard.input_scanners.anonymize import default_entity_types from llm_guard.output_scanners import ( JSON, BanSubstrings, BanTopics, Bias, Code, Deanonymize, Language, LanguageSame, MaliciousURLs, NoRefusal, Refutation, Regex, Relevance, Sensitive, ) from llm_guard.output_scanners.relevance import all_models as relevance_models from llm_guard.output_scanners.sentiment import Sentiment from llm_guard.output_scanners.toxicity import Toxicity from llm_guard.vault import Vault logger = logging.getLogger("llm-guard-playground") def init_settings() -> (List, Dict): all_scanners = [ "BanSubstrings", "BanTopics", "Bias", "Code", "Deanonymize", "JSON", "Language", "LanguageSame", "MaliciousURLs", "NoRefusal", "Refutation", "Regex", "Relevance", "Sensitive", "Sentiment", "Toxicity", ] st_enabled_scanners = st.sidebar.multiselect( "Select scanners", options=all_scanners, default=all_scanners, help="The list can be found here: https://laiyer-ai.github.io/llm-guard/output_scanners/bias/", ) settings = {} if "BanSubstrings" in st_enabled_scanners: st_bs_expander = st.sidebar.expander( "Ban Substrings", expanded=False, ) with st_bs_expander: st_bs_substrings = st.text_area( "Enter substrings to ban (one per line)", value="test\nhello\nworld\n", height=200, ).split("\n") st_bs_match_type = st.selectbox("Match type", ["str", "word"]) st_bs_case_sensitive = st.checkbox("Case sensitive", value=False) st_bs_redact = st.checkbox("Redact", value=False) st_bs_contains_all = st.checkbox("Contains all", value=False) settings["BanSubstrings"] = { "substrings": st_bs_substrings, "match_type": st_bs_match_type, "case_sensitive": st_bs_case_sensitive, "redact": st_bs_redact, "contains_all": st_bs_contains_all, } if "BanTopics" in st_enabled_scanners: st_bt_expander = st.sidebar.expander( "Ban Topics", expanded=False, ) with st_bt_expander: st_bt_topics = st_tags( label="List of topics", text="Type and press enter", value=["violence"], suggestions=[], maxtags=30, key="bt_topics", ) st_bt_threshold = st.slider( label="Threshold", value=0.6, min_value=0.0, max_value=1.0, step=0.05, key="ban_topics_threshold", ) settings["BanTopics"] = {"topics": st_bt_topics, "threshold": st_bt_threshold} if "Bias" in st_enabled_scanners: st_bias_expander = st.sidebar.expander( "Bias", expanded=False, ) with st_bias_expander: st_bias_threshold = st.slider( label="Threshold", value=0.75, min_value=0.0, max_value=1.0, step=0.05, key="bias_threshold", ) settings["Bias"] = {"threshold": st_bias_threshold} if "Code" in st_enabled_scanners: st_cd_expander = st.sidebar.expander( "Code", expanded=False, ) with st_cd_expander: st_cd_languages = st.multiselect( "Programming languages", options=["python", "java", "javascript", "go", "php", "ruby"], default=["python"], ) st_cd_mode = st.selectbox("Mode", ["allowed", "denied"], index=0) settings["Code"] = {"languages": st_cd_languages, "mode": st_cd_mode} if "JSON" in st_enabled_scanners: st_json_expander = st.sidebar.expander( "JSON", expanded=False, ) with st_json_expander: st_json_required_elements = st.slider( label="Required elements", value=0, min_value=0, max_value=10, step=1, key="json_required_elements", help="The minimum number of JSON elements that should be present", ) settings["JSON"] = {"required_elements": st_json_required_elements} if "Language" in st_enabled_scanners: st_lan_expander = st.sidebar.expander( "Language", expanded=False, ) with st_lan_expander: st_lan_valid_language = st.multiselect( "Languages", [ "af", "ar", "bg", "bn", "ca", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "fi", "fr", "gu", "he", "hi", "hr", "hu", "id", "it", "ja", "kn", "ko", "lt", "lv", "mk", "ml", "mr", "ne", "nl", "no", "pa", "pl", "pt", "ro", "ru", "sk", "sl", "so", "sq", "sv", "sw", "ta", "te", "th", "tl", "tr", "uk", "ur", "vi", "zh-cn", "zh-tw", ], default=["en"], ) settings["Language"] = { "valid_languages": st_lan_valid_language, } if "MaliciousURLs" in st_enabled_scanners: st_murls_expander = st.sidebar.expander( "Malicious URLs", expanded=False, ) with st_murls_expander: st_murls_threshold = st.slider( label="Threshold", value=0.75, min_value=0.0, max_value=1.0, step=0.05, key="murls_threshold", ) settings["MaliciousURLs"] = {"threshold": st_murls_threshold} if "NoRefusal" in st_enabled_scanners: st_no_ref_expander = st.sidebar.expander( "No refusal", expanded=False, ) with st_no_ref_expander: st_no_ref_threshold = st.slider( label="Threshold", value=0.5, min_value=0.0, max_value=1.0, step=0.05, key="no_ref_threshold", ) settings["NoRefusal"] = {"threshold": st_no_ref_threshold} if "Refutation" in st_enabled_scanners: st_refu_expander = st.sidebar.expander( "Refutation", expanded=False, ) with st_refu_expander: st_refu_threshold = st.slider( label="Threshold", value=0.5, min_value=0.0, max_value=1.0, step=0.05, key="refu_threshold", ) settings["Refutation"] = {"threshold": st_refu_threshold} if "Regex" in st_enabled_scanners: st_regex_expander = st.sidebar.expander( "Regex", expanded=False, ) with st_regex_expander: st_regex_patterns = st.text_area( "Enter patterns to ban (one per line)", value="Bearer [A-Za-z0-9-._~+/]+", height=200, ).split("\n") st_regex_type = st.selectbox( "Match type", ["good", "bad"], index=1, help="good: allow only good patterns, bad: ban bad patterns", ) st_redact = st.checkbox( "Redact", value=False, help="Replace the matched bad patterns with [REDACTED]" ) settings["Regex"] = { "patterns": st_regex_patterns, "type": st_regex_type, "redact": st_redact, } if "Relevance" in st_enabled_scanners: st_rele_expander = st.sidebar.expander( "Relevance", expanded=False, ) with st_rele_expander: st_rele_threshold = st.slider( label="Threshold", value=0.5, min_value=0.0, max_value=1.0, step=0.05, key="rele_threshold", ) st_rele_model = st.selectbox("Embeddings model", relevance_models, index=1) settings["Relevance"] = {"threshold": st_rele_threshold, "model": st_rele_model} if "Sensitive" in st_enabled_scanners: st_sens_expander = st.sidebar.expander( "Sensitive", expanded=False, ) with st_sens_expander: st_sens_entity_types = st_tags( label="Sensitive entities", text="Type and press enter", value=default_entity_types, suggestions=default_entity_types + ["DATE_TIME", "NRP", "LOCATION", "MEDICAL_LICENSE", "US_PASSPORT"], maxtags=30, key="sensitive_entity_types", ) st.caption( "Check all supported entities: https://microsoft.github.io/presidio/supported_entities/#list-of-supported-entities" ) st_sens_redact = st.checkbox("Redact", value=False, key="sens_redact") st_sens_threshold = st.slider( label="Threshold", value=0.0, min_value=0.0, max_value=1.0, step=0.1, key="sens_threshold", ) settings["Sensitive"] = { "entity_types": st_sens_entity_types, "redact": st_sens_redact, "threshold": st_sens_threshold, } if "Sentiment" in st_enabled_scanners: st_sent_expander = st.sidebar.expander( "Sentiment", expanded=False, ) with st_sent_expander: st_sent_threshold = st.slider( label="Threshold", value=-0.1, min_value=-1.0, max_value=1.0, step=0.1, key="sentiment_threshold", help="Negative values are negative sentiment, positive values are positive sentiment", ) settings["Sentiment"] = {"threshold": st_sent_threshold} if "Toxicity" in st_enabled_scanners: st_tox_expander = st.sidebar.expander( "Toxicity", expanded=False, ) with st_tox_expander: st_tox_threshold = st.slider( label="Threshold", value=0.0, min_value=-1.0, max_value=1.0, step=0.05, key="toxicity_threshold", help="A negative value (closer to 0 as the label output) indicates toxicity in the text, while a positive logit (closer to 1 as the label output) suggests non-toxicity.", ) settings["Toxicity"] = {"threshold": st_tox_threshold} return st_enabled_scanners, settings def get_scanner(scanner_name: str, vault: Vault, settings: Dict): logger.debug(f"Initializing {scanner_name} scanner") if scanner_name == "BanSubstrings": return BanSubstrings( substrings=settings["substrings"], match_type=settings["match_type"], case_sensitive=settings["case_sensitive"], redact=settings["redact"], contains_all=settings["contains_all"], ) if scanner_name == "BanTopics": return BanTopics(topics=settings["topics"], threshold=settings["threshold"]) if scanner_name == "Bias": return Bias(threshold=settings["threshold"]) if scanner_name == "Deanonymize": return Deanonymize(vault=vault) if scanner_name == "JSON": return JSON(required_elements=settings["required_elements"]) if scanner_name == "Language": return Language(valid_languages=settings["valid_languages"]) if scanner_name == "LanguageSame": return LanguageSame() if scanner_name == "Code": mode = settings["mode"] allowed_languages = None denied_languages = None if mode == "allowed": allowed_languages = settings["languages"] elif mode == "denied": denied_languages = settings["languages"] return Code(allowed=allowed_languages, denied=denied_languages) if scanner_name == "MaliciousURLs": return MaliciousURLs(threshold=settings["threshold"]) if scanner_name == "NoRefusal": return NoRefusal(threshold=settings["threshold"]) if scanner_name == "Refutation": return Refutation(threshold=settings["threshold"]) if scanner_name == "Regex": match_type = settings["type"] good_patterns = None bad_patterns = None if match_type == "good": good_patterns = settings["patterns"] elif match_type == "bad": bad_patterns = settings["patterns"] return Regex( good_patterns=good_patterns, bad_patterns=bad_patterns, redact=settings["redact"] ) if scanner_name == "Relevance": return Relevance(threshold=settings["threshold"], model=settings["model"]) if scanner_name == "Sensitive": return Sensitive( entity_types=settings["entity_types"], redact=settings["redact"], threshold=settings["threshold"], ) if scanner_name == "Sentiment": return Sentiment(threshold=settings["threshold"]) if scanner_name == "Toxicity": return Toxicity(threshold=settings["threshold"]) raise ValueError("Unknown scanner name") def scan( vault: Vault, enabled_scanners: List[str], settings: Dict, prompt: str, text: str, fail_fast: bool = False, ) -> (str, Dict[str, bool], Dict[str, float]): sanitized_output = text results_valid = {} results_score = {} status_text = "Scanning prompt..." if fail_fast: status_text = "Scanning prompt (fail fast mode)..." with st.status(status_text, expanded=True) as status: for scanner_name in enabled_scanners: st.write(f"{scanner_name} scanner...") scanner = get_scanner( scanner_name, vault, settings[scanner_name] if scanner_name in settings else {} ) sanitized_output, is_valid, risk_score = scanner.scan(prompt, sanitized_output) results_valid[scanner_name] = is_valid results_score[scanner_name] = risk_score if fail_fast and not is_valid: break status.update(label="Scanning complete", state="complete", expanded=False) return sanitized_output, results_valid, results_score