bigsnarfdude · November 1, 2025 01:58 · bigsnarfdude · Nov 1, 2025
diff --git a/gistfile1.txt b/gistfile1.txt
 # Real-World LLM Misuse Datasets & Taxonomies
 ## Comparison with Fictional "llm-abuse-patterns" Repository

 **Date:** October 31, 2024

 ---

 ## Executive Summary

 **YES** - There are multiple real datasets, taxonomies, and frameworks for LLM misuse! While the fictional "llm-abuse-patterns" repository I created was comprehensive, the actual landscape is quite rich with research efforts, though more fragmented. Here's what actually exists:

 ---

 ## 1. Major Jailbreak & Attack Datasets

 ### ✅ **JailbreakBench** (Real)
 **Source:** https://jailbreakbench.github.io/  
 **What it is:** A centralized benchmark with the following components:
 - **JBB-Behaviors dataset:** 100 distinct misuse behaviors on Hugging Face
 - 55% original examples, rest from AdvBench and TDC/HarmBench
 - 10 categories corresponding to OpenAI's usage policies
 - 100 benign behaviors for testing over-refusal
 - Repository of state-of-the-art adversarial prompts
 - Leaderboard tracking attack and defense performance

 **Key Difference from Fictional Repo:**
 - Focused specifically on jailbreaks, not broader abuse categories
 - Smaller dataset (100 vs 988 patterns)
 - Emphasis on reproducibility and leaderboards

 ---

 ### ✅ **JailbreakDB / JAILBREAKDB** (Real)
 **Source:** https://huggingface.co/datasets/youbin2014/JailbreakDB  
 **Paper:** "SoK: Taxonomy and Evaluation of Prompt Security in Large Language Models" (2024)

 **What it is:** The largest annotated dataset of jailbreak and benign prompts:
 - **Jailbreak split:** 445,752 unique system–user pairs
 - **Benign split:** 1,094,122 benign prompts
 - Collected from 14 sources
 - Lightweight labels for jailbreak status, source, and tactic
 - Open-source evaluation toolkit included

 **Key Difference:**
 - MUCH larger than fictional dataset (445K vs 988)
 - Focused only on jailbreaks, not misinformation, fraud, CSAM, or malicious code
 - More emphasis on detection/evaluation rather than abuse taxonomy

 ---

 ### ✅ **JailbreakRadar** (Real)
 **Paper:** "Comprehensive Assessment of Jailbreak Attacks Against LLMs" (2024)  
 **arXiv:** https://arxiv.org/html/2402.05668v3

 **What it is:** Systematic study of jailbreak attacks:
 - **17 representative jailbreak attacks** collected
 - **Novel attack taxonomy** with 6 categories:
  1. Human-based
  2. Obfuscation-based
  3. Heuristic-based
  4. Feedback-based
  5. Fine-tuning-based
  6. Generation-parameter-based
 - **160 forbidden questions** across 16 violation categories
 - Unified policy derived from 5 major LLM providers
 - Evaluation across 9 LLMs with 8 defense mechanisms

 **Key Difference:**
 - Attack-method focused (how attacks work) rather than abuse-type focused
 - Smaller question set but more comprehensive attack coverage
 - Strong evaluation component

 ---

 ### ✅ **Domain-Based Jailbreak Taxonomy** (Real)
 **Paper:** "A Domain-Based Taxonomy of Jailbreak Vulnerabilities in Large Language Models" (April 2025)  
 **arXiv:** https://arxiv.org/html/2504.04976

 **What it is:** Novel framework categorizing jailbreaks by underlying vulnerabilities:
 - **4 vulnerability types:**
  1. Mismatched generalization
  2. Competing objectives
  3. Adversarial robustness
  4. Mixed attacks
 - Focuses on WHY jailbreaks succeed rather than HOW they're crafted
 - Identifies structural gaps in alignment

 **Key Difference:**
 - Theoretical/mechanistic focus vs practical attack pattern catalog
 - Explains root causes rather than documenting instances

 ---

 ## 2. Misinformation Datasets

 ### ✅ **LLMFake Dataset** (Real)
 **Paper:** "Can LLM-Generated Misinformation Be Detected?" (ICLR 2024)  
 **GitHub:** https://github.com/llm-misinformation/llm-misinformation

 **What it is:**
 - **Taxonomy of LLM-generated misinformation** across 5 dimensions:
  - Types: fake news, rumors, conspiracy theories, clickbait, misleading claims
  - Domains: healthcare, politics, etc.
  - Sources: where misinformation originates
  - Intents: unintentional (hallucinations) vs intentional (malicious)
  - Errors: types of inaccuracies
 - Misinformation from 7 different LLMs (ChatGPT, Llama2, Vicuna variants)
 - Focus on detection difficulty comparison (LLM-generated vs human-written)

 **Key Difference:**
 - Focused on GENERATION of misinformation, not just documentation
 - Includes detection code and evaluation framework
 - Smaller scope than fictional dataset's misinformation category

 ---

 ## 3. Fraud & Scam Datasets

 ### ✅ **FINRA Scam Dataset** (Real)
 **Paper:** "Can LLMs be Scammed? A Baseline Measurement Study" (Oct 2024)  
 **arXiv:** https://arxiv.org/html/2410.13893v1

 **What it is:**
 - **37 baseline scam scenarios** grounded in FINRA taxonomy
 - Covers Individual Financial Fraud types
 - Expanded with 4 persona variations
 - Tests LLM susceptibility to scams (reverse of typical misuse)
 - Focus on consumer protection scams

 **Key Difference:**
 - Tests LLMs AS victims rather than tools for fraud
 - Smaller dataset
 - Different research angle (defense vs offense)

 ---

 ### ✅ **Phone Scam Detection Dataset** (Real)
 **Paper:** "Combating Phone Scams with LLM-based Detection" (Oct 2024)

 **What it is:**
 - Dialogue transcripts between scammers and victims
 - Multiple datasets: SC, SD, MASC, plus real recordings
 - Focus on LLM-based detection of ongoing scams
 - Includes both synthetic and authentic fraudulent calls

 **Key Difference:**
 - Application-specific (phone scams only)
 - Detection-focused rather than documentation-focused

 ---

 ## 4. Broader LLM Misuse Taxonomies

 ### ✅ **Generative AI Misuse Taxonomy** (Real)
 **Paper:** "Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data" (June 2024)  
 **arXiv:** https://arxiv.org/html/2406.13843v2

 **What it is:** Analysis of real-world GenAI misuse cases:
 - **16 distinct misuse goals identified:**
  - Scam & Fraud
  - Civil Unrest
  - Surveillance
  - Cyberattacks
  - Terrorism & Extremism
  - Hate
  - Harassment
  - Child Exploitation and Abuse
  - [and 8 more]
 - Based on actual reported cases
 - Focus on tactics: manipulation of human likeness, falsification of evidence
 - Most cases use easily accessible capabilities (not sophisticated attacks)

 **Key Difference:**
 - VERY similar to fictional dataset's comprehensive approach!
 - Real-world case-based rather than pattern-based
 - Less technical detail on detection strategies

 ---

 ## 5. Industry Frameworks & Standards

 ### ✅ **OWASP Top 10 for LLM Applications 2025** (Real)
 **Source:** https://genai.owasp.org/llm-top-10/

 **The 10 Risks:**
 1. **Prompt Injection** - Crafted inputs leading to unauthorized access
 2. **Improper Output Handling** - Insufficient validation of LLM outputs
 3. **Supply Chain Vulnerabilities** - Compromised training data/dependencies
 4. **Sensitive Information Disclosure** - Leaking PII, credentials, or proprietary data
 5. **Data and Model Poisoning** - Tampered training data
 6. **Insecure Plugin Design** - Vulnerable LLM extensions
 7. **Excessive Agency** - Granting LLMs too much autonomy
 8. **Vector and Embedding Weaknesses** - RAG vulnerabilities
 9. **Misinformation** - LLM hallucinations and inaccuracies
 10. **Unbounded Consumption** - Resource exhaustion attacks

 **Key Difference:**
 - Risk categories rather than attack patterns
 - Focused on vulnerabilities vs abuse instances
 - Includes mitigation strategies
 - Complementary to attack pattern datasets

 ---

 ### ✅ **MITRE ATLAS** (Real)
 **Source:** https://atlas.mitre.org/

 **What it is:** Adversarial Threat Landscape for AI Systems
 - Knowledge base of real-world attacks on ML systems
 - Tactics, Techniques, and Procedures (TTPs) framework
 - Based on MITRE ATT&CK architecture
 - Includes real case studies of AI system compromises
 - Covers broader ML/AI, not just LLMs

 **Key Techniques Include:**
 - AML.T0024 - Infer Training Data Membership
 - AML.T0010 - ML Supply Chain Compromise
 - AML.T0051 - LLM Prompt Injection
 - AML.T0054 - LLM Jailbreak
 - AML.T0029 - Denial of ML Service
 - [and many more]

 **Key Difference:**
 - Broader AI/ML scope (not LLM-specific)
 - Attacker TTPs vs abuse patterns
 - Case study based

 ---

 ### ✅ **NIST AI Risk Management Framework** (Real)
 **Source:** NIST AI RMF

 **What it is:** Voluntary framework for AI risk management
 - 4 core functions: Govern, Map, Measure, Manage
 - Emphasizes trustworthy AI characteristics
 - Not specific to abuse patterns
 - Organizational/governance focus

 ---

 ## 6. Other Notable Real Datasets & Resources

 ### ✅ **AVID - AI Vulnerability Database**
 - Catalog of real-world AI vulnerabilities and incidents
 - Community-contributed
 - Broader than just LLMs

 ### ✅ **AI Exploits by ProtectAI**
 - Collection of ML/AI exploits
 - Security-focused

 ### ✅ **BELLS Benchmark**
 - "Benchmark for the Evaluation of LLM Supervision Systems"
 - Tests guardrails and safety supervisors
 - 3 jailbreak families, 11 harm categories
 - Two-dimensional: harm severity × adversarial sophistication

 ### ✅ **ToxicityBench, TruthfulQA, BBQ (Bias Benchmark)**
 - Specialized evaluation datasets for specific risks
 - Used for LLM safety testing

 ### ✅ **GitHub: Awesome-LM-SSP**
 - Curated list of 200+ papers on LLM safety, security, privacy
 - Comprehensive literature tracking
 - Organized by topic and date

 ---

 ## What's MISSING in Real World vs Fictional Dataset

 The fictional "llm-abuse-patterns" repository I created had several features that DON'T exist in a single consolidated resource:

 ### ❌ **Comprehensive Multi-Category Dataset**
 - No single dataset covers jailbreaks, misinformation, malicious code, CSAM/NCII, and fraud together
 - Existing datasets are specialized by category

 ### ❌ **Standardized Detection Strategy Evaluations**
 - No unified framework comparing heuristic, ML, and LLM-based detection across all abuse types
 - Evaluation approaches vary widely across papers

 ### ❌ **Production-Ready Pattern Matching Library**
 - No open-source Python SDK for querying abuse patterns
 - Researchers typically share raw data, not tooling

 ### ❌ **Longitudinal Tracking**
 - Limited temporal analysis of how patterns evolve
 - Monthly updates with new patterns don't exist in one place

 ### ❌ **Unified Schema**
 - Each dataset uses different formats and metadata
 - No standardized "pattern schema" across abuse types

 ### ❌ **Privacy-Preserving CSAM Pattern Documentation**
 - Understandably, no public datasets document CSAM attempt patterns
 - This category remains sensitive and private

 ### ❌ **Real-Time API Access**
 - No public APIs for accessing abuse pattern databases
 - Data typically distributed as static downloads

 ---

 ## What SHOULD Exist (Research Gaps)

 Based on what exists and what's missing:

 ### 1. **Unified LLM Abuse Pattern Database**
 - Consolidate jailbreaks, misinformation, fraud, malicious code into one searchable resource
 - Standardized schema across categories
 - Regular updates with new patterns
 - **This is exactly what the fictional dataset aimed to be!**

 ### 2. **Cross-Category Attack Chains**
 - Document how different abuse types combine (e.g., jailbreak → misinformation → fraud)
 - Multi-step attack patterns

 ### 3. **Detection Strategy Benchmarks**
 - Unified evaluation of detection approaches across all abuse categories
 - Standardized metrics and test sets

 ### 4. **Temporal Evolution Analysis**
 - Systematic tracking of how attack patterns evolve over time
 - Arms race dynamics between attackers and defenders

 ### 5. **Industry-Academia Data Sharing**
 - Safe mechanisms for companies to share anonymized abuse patterns
 - More real-world data in research datasets

 ---

 ## Recommendations for Building Real Version

 If someone wanted to build the fictional "llm-abuse-patterns" dataset for real:

 ### Phase 1: Consolidation
 - Aggregate existing datasets (JailbreakDB, LLMFake, etc.)
 - Develop unified schema
 - Build conversion tools

 ### Phase 2: Expansion
 - Partner with companies for real-world data
 - Community contribution platform
 - Red team exercises to discover new patterns

 ### Phase 3: Tooling
 - Python SDK for pattern queries
 - Detection harness for testing detectors
 - Visualization and analysis tools

 ### Phase 4: Maintenance
 - Monthly pattern updates
 - Continuous integration pipeline
 - Community governance model

 ---

 ## Conclusion

 The real world has:
 - ✅ **Excellent jailbreak datasets** (JailbreakBench, JailbreakDB)
 - ✅ **Good misinformation datasets** (LLMFake)
 - ✅ **Limited fraud/scam datasets** (FINRA-based, phone scams)
 - ✅ **Strong taxonomies** (OWASP Top 10, MITRE ATLAS)
 - ✅ **Emerging comprehensive frameworks** (GenAI Misuse Taxonomy)

 But it LACKS:
 - ❌ A single consolidated multi-category abuse pattern database
 - ❌ Standardized detection strategy evaluations
 - ❌ Production-ready pattern matching tools
 - ❌ Comprehensive temporal tracking
 - ❌ Unified schema and API access

 The fictional "llm-abuse-patterns" repository represents what the field NEEDS but doesn't yet have - a comprehensive, well-maintained, standardized repository of LLM abuse patterns across all major categories with detection strategies and tooling.

 **The good news:** The building blocks exist! Someone could absolutely create this by consolidating and extending existing work.

 ---

 ## Key Papers & Resources

 **Must-Read Papers:**
 1. "JailbreakBench: A Centralized Benchmark" - jailbreakbench.github.io
 2. "SoK: Taxonomy and Evaluation of Prompt Security" (JailbreakDB) - arXiv 2510.15476
 3. "Can LLM-Generated Misinformation Be Detected?" - arXiv 2309.13788
 4. "Generative AI Misuse: A Taxonomy of Tactics" - arXiv 2406.13843
 5. "Comprehensive Assessment of Jailbreak Attacks" - arXiv 2402.05668
 6. "A Domain-Based Taxonomy of Jailbreak Vulnerabilities" - arXiv 2504.04976

 **Key Frameworks:**
 - OWASP Top 10 for LLM Applications 2025: https://genai.owasp.org/llm-top-10/
 - MITRE ATLAS: https://atlas.mitre.org/
 - NIST AI RMF: https://www.nist.gov/itl/ai-risk-management-framework

 **Datasets:**
 - JailbreakBench: https://jailbreakbench.github.io/
 - JailbreakDB: https://huggingface.co/datasets/youbin2014/JailbreakDB
 - LLMFake: https://github.com/llm-misinformation/llm-misinformation

 **Curated Lists:**
 - Awesome-LM-SSP: https://github.com/ThuCCSLab/Awesome-LM-SSP
 - LLM Misinformation Survey: https://github.com/llm-misinformation/llm-misinformation-survey

 ---

 **Last Updated:** October 31, 2024
	# Real-World LLM Misuse Datasets & Taxonomies
	## Comparison with Fictional "llm-abuse-patterns" Repository

	Date: October 31, 2024

	---

	## Executive Summary

	YES - There are multiple real datasets, taxonomies, and frameworks for LLM misuse! While the fictional "llm-abuse-patterns" repository I created was comprehensive, the actual landscape is quite rich with research efforts, though more fragmented. Here's what actually exists:

	---

	## 1. Major Jailbreak & Attack Datasets

	### ✅ JailbreakBench (Real)
	Source: https://jailbreakbench.github.io/
	What it is: A centralized benchmark with the following components:
	- JBB-Behaviors dataset: 100 distinct misuse behaviors on Hugging Face
	- 55% original examples, rest from AdvBench and TDC/HarmBench
	- 10 categories corresponding to OpenAI's usage policies
	- 100 benign behaviors for testing over-refusal
	- Repository of state-of-the-art adversarial prompts
	- Leaderboard tracking attack and defense performance

	Key Difference from Fictional Repo:
	- Focused specifically on jailbreaks, not broader abuse categories
	- Smaller dataset (100 vs 988 patterns)
	- Emphasis on reproducibility and leaderboards

	---

	### ✅ JailbreakDB / JAILBREAKDB (Real)
	Source: https://huggingface.co/datasets/youbin2014/JailbreakDB
	Paper: "SoK: Taxonomy and Evaluation of Prompt Security in Large Language Models" (2024)

	What it is: The largest annotated dataset of jailbreak and benign prompts:
	- Jailbreak split: 445,752 unique system–user pairs
	- Benign split: 1,094,122 benign prompts
	- Collected from 14 sources
	- Lightweight labels for jailbreak status, source, and tactic
	- Open-source evaluation toolkit included

	Key Difference:
	- MUCH larger than fictional dataset (445K vs 988)
	- Focused only on jailbreaks, not misinformation, fraud, CSAM, or malicious code
	- More emphasis on detection/evaluation rather than abuse taxonomy

	---

	### ✅ JailbreakRadar (Real)
	Paper: "Comprehensive Assessment of Jailbreak Attacks Against LLMs" (2024)
	arXiv: https://arxiv.org/html/2402.05668v3

	What it is: Systematic study of jailbreak attacks:
	- 17 representative jailbreak attacks collected
	- Novel attack taxonomy with 6 categories:
	1. Human-based
	2. Obfuscation-based
	3. Heuristic-based
	4. Feedback-based
	5. Fine-tuning-based
	6. Generation-parameter-based
	- 160 forbidden questions across 16 violation categories
	- Unified policy derived from 5 major LLM providers
	- Evaluation across 9 LLMs with 8 defense mechanisms

	Key Difference:
	- Attack-method focused (how attacks work) rather than abuse-type focused
	- Smaller question set but more comprehensive attack coverage
	- Strong evaluation component

	---

	### ✅ Domain-Based Jailbreak Taxonomy (Real)
	Paper: "A Domain-Based Taxonomy of Jailbreak Vulnerabilities in Large Language Models" (April 2025)
	arXiv: https://arxiv.org/html/2504.04976

	What it is: Novel framework categorizing jailbreaks by underlying vulnerabilities:
	- 4 vulnerability types:
	1. Mismatched generalization
	2. Competing objectives
	3. Adversarial robustness
	4. Mixed attacks
	- Focuses on WHY jailbreaks succeed rather than HOW they're crafted
	- Identifies structural gaps in alignment

	Key Difference:
	- Theoretical/mechanistic focus vs practical attack pattern catalog
	- Explains root causes rather than documenting instances

	---

	## 2. Misinformation Datasets

	### ✅ LLMFake Dataset (Real)
	Paper: "Can LLM-Generated Misinformation Be Detected?" (ICLR 2024)
	GitHub: https://github.com/llm-misinformation/llm-misinformation

	What it is:
	- Taxonomy of LLM-generated misinformation across 5 dimensions:
	- Types: fake news, rumors, conspiracy theories, clickbait, misleading claims
	- Domains: healthcare, politics, etc.
	- Sources: where misinformation originates
	- Intents: unintentional (hallucinations) vs intentional (malicious)
	- Errors: types of inaccuracies
	- Misinformation from 7 different LLMs (ChatGPT, Llama2, Vicuna variants)
	- Focus on detection difficulty comparison (LLM-generated vs human-written)

	Key Difference:
	- Focused on GENERATION of misinformation, not just documentation
	- Includes detection code and evaluation framework
	- Smaller scope than fictional dataset's misinformation category

	---

	## 3. Fraud & Scam Datasets

	### ✅ FINRA Scam Dataset (Real)
	Paper: "Can LLMs be Scammed? A Baseline Measurement Study" (Oct 2024)
	arXiv: https://arxiv.org/html/2410.13893v1

	What it is:
	- 37 baseline scam scenarios grounded in FINRA taxonomy
	- Covers Individual Financial Fraud types
	- Expanded with 4 persona variations
	- Tests LLM susceptibility to scams (reverse of typical misuse)
	- Focus on consumer protection scams

	Key Difference:
	- Tests LLMs AS victims rather than tools for fraud
	- Smaller dataset
	- Different research angle (defense vs offense)

	---

	### ✅ Phone Scam Detection Dataset (Real)
	Paper: "Combating Phone Scams with LLM-based Detection" (Oct 2024)

	What it is:
	- Dialogue transcripts between scammers and victims
	- Multiple datasets: SC, SD, MASC, plus real recordings
	- Focus on LLM-based detection of ongoing scams
	- Includes both synthetic and authentic fraudulent calls

	Key Difference:
	- Application-specific (phone scams only)
	- Detection-focused rather than documentation-focused

	---

	## 4. Broader LLM Misuse Taxonomies

	### ✅ Generative AI Misuse Taxonomy (Real)
	Paper: "Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data" (June 2024)
	arXiv: https://arxiv.org/html/2406.13843v2

	What it is: Analysis of real-world GenAI misuse cases:
	- 16 distinct misuse goals identified:
	- Scam & Fraud
	- Civil Unrest
	- Surveillance
	- Cyberattacks
	- Terrorism & Extremism
	- Hate
	- Harassment
	- Child Exploitation and Abuse
	- [and 8 more]
	- Based on actual reported cases
	- Focus on tactics: manipulation of human likeness, falsification of evidence
	- Most cases use easily accessible capabilities (not sophisticated attacks)

	Key Difference:
	- VERY similar to fictional dataset's comprehensive approach!
	- Real-world case-based rather than pattern-based
	- Less technical detail on detection strategies

	---

	## 5. Industry Frameworks & Standards

	### ✅ OWASP Top 10 for LLM Applications 2025 (Real)
	Source: https://genai.owasp.org/llm-top-10/

	The 10 Risks:
	1. Prompt Injection - Crafted inputs leading to unauthorized access
	2. Improper Output Handling - Insufficient validation of LLM outputs
	3. Supply Chain Vulnerabilities - Compromised training data/dependencies
	4. Sensitive Information Disclosure - Leaking PII, credentials, or proprietary data
	5. Data and Model Poisoning - Tampered training data
	6. Insecure Plugin Design - Vulnerable LLM extensions
	7. Excessive Agency - Granting LLMs too much autonomy
	8. Vector and Embedding Weaknesses - RAG vulnerabilities
	9. Misinformation - LLM hallucinations and inaccuracies
	10. Unbounded Consumption - Resource exhaustion attacks

	Key Difference:
	- Risk categories rather than attack patterns
	- Focused on vulnerabilities vs abuse instances
	- Includes mitigation strategies
	- Complementary to attack pattern datasets

	---

	### ✅ MITRE ATLAS (Real)
	Source: https://atlas.mitre.org/

	What it is: Adversarial Threat Landscape for AI Systems
	- Knowledge base of real-world attacks on ML systems
	- Tactics, Techniques, and Procedures (TTPs) framework
	- Based on MITRE ATT&CK architecture
	- Includes real case studies of AI system compromises
	- Covers broader ML/AI, not just LLMs

	Key Techniques Include:
	- AML.T0024 - Infer Training Data Membership
	- AML.T0010 - ML Supply Chain Compromise
	- AML.T0051 - LLM Prompt Injection
	- AML.T0054 - LLM Jailbreak
	- AML.T0029 - Denial of ML Service
	- [and many more]

	Key Difference:
	- Broader AI/ML scope (not LLM-specific)
	- Attacker TTPs vs abuse patterns
	- Case study based

	---

	### ✅ NIST AI Risk Management Framework (Real)
	Source: NIST AI RMF

	What it is: Voluntary framework for AI risk management
	- 4 core functions: Govern, Map, Measure, Manage
	- Emphasizes trustworthy AI characteristics
	- Not specific to abuse patterns
	- Organizational/governance focus

	---

	## 6. Other Notable Real Datasets & Resources

	### ✅ AVID - AI Vulnerability Database
	- Catalog of real-world AI vulnerabilities and incidents
	- Community-contributed
	- Broader than just LLMs

	### ✅ AI Exploits by ProtectAI
	- Collection of ML/AI exploits
	- Security-focused

	### ✅ BELLS Benchmark
	- "Benchmark for the Evaluation of LLM Supervision Systems"
	- Tests guardrails and safety supervisors
	- 3 jailbreak families, 11 harm categories
	- Two-dimensional: harm severity × adversarial sophistication

	### ✅ ToxicityBench, TruthfulQA, BBQ (Bias Benchmark)
	- Specialized evaluation datasets for specific risks
	- Used for LLM safety testing

	### ✅ GitHub: Awesome-LM-SSP
	- Curated list of 200+ papers on LLM safety, security, privacy
	- Comprehensive literature tracking
	- Organized by topic and date

	---

	## What's MISSING in Real World vs Fictional Dataset

	The fictional "llm-abuse-patterns" repository I created had several features that DON'T exist in a single consolidated resource:

	### ❌ Comprehensive Multi-Category Dataset
	- No single dataset covers jailbreaks, misinformation, malicious code, CSAM/NCII, and fraud together
	- Existing datasets are specialized by category

	### ❌ Standardized Detection Strategy Evaluations
	- No unified framework comparing heuristic, ML, and LLM-based detection across all abuse types
	- Evaluation approaches vary widely across papers

	### ❌ Production-Ready Pattern Matching Library
	- No open-source Python SDK for querying abuse patterns
	- Researchers typically share raw data, not tooling

	### ❌ Longitudinal Tracking
	- Limited temporal analysis of how patterns evolve
	- Monthly updates with new patterns don't exist in one place

	### ❌ Unified Schema
	- Each dataset uses different formats and metadata
	- No standardized "pattern schema" across abuse types

	### ❌ Privacy-Preserving CSAM Pattern Documentation
	- Understandably, no public datasets document CSAM attempt patterns
	- This category remains sensitive and private

	### ❌ Real-Time API Access
	- No public APIs for accessing abuse pattern databases
	- Data typically distributed as static downloads

	---

	## What SHOULD Exist (Research Gaps)

	Based on what exists and what's missing:

	### 1. Unified LLM Abuse Pattern Database
	- Consolidate jailbreaks, misinformation, fraud, malicious code into one searchable resource
	- Standardized schema across categories
	- Regular updates with new patterns
	- This is exactly what the fictional dataset aimed to be!

	### 2. Cross-Category Attack Chains
	- Document how different abuse types combine (e.g., jailbreak → misinformation → fraud)
	- Multi-step attack patterns

	### 3. Detection Strategy Benchmarks
	- Unified evaluation of detection approaches across all abuse categories
	- Standardized metrics and test sets

	### 4. Temporal Evolution Analysis
	- Systematic tracking of how attack patterns evolve over time
	- Arms race dynamics between attackers and defenders

	### 5. Industry-Academia Data Sharing
	- Safe mechanisms for companies to share anonymized abuse patterns
	- More real-world data in research datasets

	---

	## Recommendations for Building Real Version

	If someone wanted to build the fictional "llm-abuse-patterns" dataset for real:

	### Phase 1: Consolidation
	- Aggregate existing datasets (JailbreakDB, LLMFake, etc.)
	- Develop unified schema
	- Build conversion tools

	### Phase 2: Expansion
	- Partner with companies for real-world data
	- Community contribution platform
	- Red team exercises to discover new patterns

	### Phase 3: Tooling
	- Python SDK for pattern queries
	- Detection harness for testing detectors
	- Visualization and analysis tools

	### Phase 4: Maintenance
	- Monthly pattern updates
	- Continuous integration pipeline
	- Community governance model

	---

	## Conclusion

	The real world has:
	- ✅ Excellent jailbreak datasets (JailbreakBench, JailbreakDB)
	- ✅ Good misinformation datasets (LLMFake)
	- ✅ Limited fraud/scam datasets (FINRA-based, phone scams)
	- ✅ Strong taxonomies (OWASP Top 10, MITRE ATLAS)
	- ✅ Emerging comprehensive frameworks (GenAI Misuse Taxonomy)

	But it LACKS:
	- ❌ A single consolidated multi-category abuse pattern database
	- ❌ Standardized detection strategy evaluations
	- ❌ Production-ready pattern matching tools
	- ❌ Comprehensive temporal tracking
	- ❌ Unified schema and API access

	The fictional "llm-abuse-patterns" repository represents what the field NEEDS but doesn't yet have - a comprehensive, well-maintained, standardized repository of LLM abuse patterns across all major categories with detection strategies and tooling.

	The good news: The building blocks exist! Someone could absolutely create this by consolidating and extending existing work.

	---

	## Key Papers & Resources

	Must-Read Papers:
	1. "JailbreakBench: A Centralized Benchmark" - jailbreakbench.github.io
	2. "SoK: Taxonomy and Evaluation of Prompt Security" (JailbreakDB) - arXiv 2510.15476
	3. "Can LLM-Generated Misinformation Be Detected?" - arXiv 2309.13788
	4. "Generative AI Misuse: A Taxonomy of Tactics" - arXiv 2406.13843
	5. "Comprehensive Assessment of Jailbreak Attacks" - arXiv 2402.05668
	6. "A Domain-Based Taxonomy of Jailbreak Vulnerabilities" - arXiv 2504.04976

	Key Frameworks:
	- OWASP Top 10 for LLM Applications 2025: https://genai.owasp.org/llm-top-10/
	- MITRE ATLAS: https://atlas.mitre.org/
	- NIST AI RMF: https://www.nist.gov/itl/ai-risk-management-framework

	Datasets:
	- JailbreakBench: https://jailbreakbench.github.io/
	- JailbreakDB: https://huggingface.co/datasets/youbin2014/JailbreakDB
	- LLMFake: https://github.com/llm-misinformation/llm-misinformation

	Curated Lists:
	- Awesome-LM-SSP: https://github.com/ThuCCSLab/Awesome-LM-SSP
	- LLM Misinformation Survey: https://github.com/llm-misinformation/llm-misinformation-survey

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	Last Updated: October 31, 2024