* attacks

docs/changelog.md

@@ -12,6 +12,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 - `Anonymize`: language support with `zh` ([#79](https://github.com/laiyer-ai/llm-guard/pull/79), thanks to [@Oscaner](https://github.com/Oscaner)).
 - `Anonymize`: more regex patterns, such as `PO_BOX_RE`, `PRICE_RE`, `HEX_COLOR`, `TIME_RE`, `DATE_RE`, `URL_RE`, `PHONE_NUMBER_WITH_EXT`, `BTC_ADDRESS`
+- Add [NIST Taxonomy](./get_started/attacks.md) to the documentation.
 
 ### Fixed
 - Incorrect results when using `Deanonymize` multiple times ([#82](https://github.com/laiyer-ai/llm-guard/pull/82), thanks to [@andreaponti5](https://github.com/andreaponti5))

docs/get_started/attacks.md

@@ -0,0 +1,47 @@
+# Attacks
+
+This section outlines the range of attacks that can be launched against Large Language Models (LLMs) and demonstrates how LLM Guard offers robust protection against these threats.
+
+## NIST Trustworthy and Responsible AI
+
+Following the [NIST Trustworthy and Responsible AI framework](https://doi.org/10.6028/NIST.AI.100-2e2023), attacks on Generative AI systems, including LLMs, can be broadly categorized into four types.
+LLM Guard is designed to counteract each category effectively:
+
+### 1. Availability Breakdowns
+
+Attacks targeting the availability of LLMs aim to disrupt their normal operations. Methods such as Denial of Service (DoS) attacks are common. LLM Guard combats these through:
+
+- [TokenLimit Input](../input_scanners/token_limit.md)
+- ...
+
+### 2. Integrity Violations
+
+These attacks attempt to undermine the integrity of LLMs, often by injecting malicious prompts. LLM Guard safeguards integrity through various scanners, including:
+
+- [Prompt Injection](../input_scanners/prompt_injection.md)
+- Language [Input](../input_scanners/language.md) & [Output](../output_scanners/language.md)
+- [Language Same](../output_scanners/language_same.md)
+- [Relevance Output](../output_scanners/relevance.md)
+- [Factual Consistency Output](../output_scanners/factual_consistency.md)
+- Ban Topics [Input](../input_scanners/ban_topics.md) & [Output](../output_scanners/ban_topics.md)
+- ...
+
+### 3. Privacy Compromise
+
+These attacks seek to compromise privacy by extracting sensitive information from LLMs. LLM Guard protects privacy through:
+
+- [Anonymize Input](../input_scanners/anonymize.md)
+- [Sensitive Output](../output_scanners/sensitive.md)
+- [Secrets Input](../input_scanners/secrets.md)
+- ...
+
+### 4. Abuse
+
+Abuse attacks involve the generation of harmful content using LLMs. LLM Guard mitigates these risks through:
+
+- [Bias Output](../output_scanners/bias.md)
+- Toxicity [Input](../input_scanners/toxicity.md) & [Output](../output_scanners/toxicity.md)
+- Ban Competitors [Input](../input_scanners/ban_competitors.md) & [Output](../output_scanners/ban_competitors.md)
+- ...
+
+LLM Guard's suite of scanners comprehensively addresses each category of attack, providing a multi-layered defense mechanism to ensure the safe and responsible use of LLMs.