docs: add proof pudding cve (#424)

Author: GangGreenTemperTatum

2_0_vulns/LLM02_ImproperOutputHandling.md

@@ -37,12 +37,13 @@ The following conditions can increase the impact of this vulnerability:
 
 ### Reference Links
 
-1. [Arbitrary Code Execution](https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5411357): **Snyk Security Blog**
-2. [ChatGPT Plugin Exploit Explained: From Prompt Injection to Accessing Private Data](https://embracethered.com/blog/posts/2023/chatgpt-cross-plugin-request-forgery-and-prompt-injection./): **Embrace The Red**
-3. [New prompt injection attack on ChatGPT web version. Markdown images can steal your chat data.](https://systemweakness.com/new-prompt-injection-attack-on-chatgpt-web-version-ef717492c5c2?gi=8daec85e2116): **System Weakness**
-4. [Don’t blindly trust LLM responses. Threats to chatbots](https://embracethered.com/blog/posts/2023/ai-injections-threats-context-matters/): **Embrace The Red**
-5. [Threat Modeling LLM Applications](https://aivillage.org/large%20language%20models/threat-modeling-llm/): **AI Village**
-6. [OWASP ASVS - 5 Validation, Sanitization and Encoding](https://owasp-aasvs4.readthedocs.io/en/latest/V5.html#validation-sanitization-and-encoding): **OWASP AASVS**
-7. [AI hallucinates software packages and devs download them – even if potentially poisoned with malware](https://www.theregister.com/2024/03/28/ai_bots_hallucinate_software_packages/) **Theregiste**
+1. [Proof Pudding (CVE-2019-20634)](https://avidml.org/database/avid-2023-v009/) **AVID** (`moohax` & `monoxgas`)
+2. [Arbitrary Code Execution](https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5411357): **Snyk Security Blog**
+3. [ChatGPT Plugin Exploit Explained: From Prompt Injection to Accessing Private Data](https://embracethered.com/blog/posts/2023/chatgpt-cross-plugin-request-forgery-and-prompt-injection./): **Embrace The Red**
+4. [New prompt injection attack on ChatGPT web version. Markdown images can steal your chat data.](https://systemweakness.com/new-prompt-injection-attack-on-chatgpt-web-version-ef717492c5c2?gi=8daec85e2116): **System Weakness**
+5. [Don’t blindly trust LLM responses. Threats to chatbots](https://embracethered.com/blog/posts/2023/ai-injections-threats-context-matters/): **Embrace The Red**
+6. [Threat Modeling LLM Applications](https://aivillage.org/large%20language%20models/threat-modeling-llm/): **AI Village**
+7. [OWASP ASVS - 5 Validation, Sanitization and Encoding](https://owasp-aasvs4.readthedocs.io/en/latest/V5.html#validation-sanitization-and-encoding): **OWASP AASVS**
+8. [AI hallucinates software packages and devs download them – even if potentially poisoned with malware](https://www.theregister.com/2024/03/28/ai_bots_hallucinate_software_packages/) **Theregiste**
 
 

2_0_vulns/LLM04_UnboundedConsumption.md

@@ -56,22 +56,21 @@ Unbounded Consumption occurs when a Large Language Model (LLM) application allow
 
 ### Reference Links
 
-1. [arXiv:2403.06634 Stealing Part of a Production Language Model](https://arxiv.org/abs/2403.06634) **arXiv**
-2. [Runaway LLaMA | How Meta's LLaMA NLP model leaked](https://www.deeplearning.ai/the-batch/how-metas-llama-nlp-model-leaked/): **Deep Learning Blog**
-3. [AML.TA0000 ML Model Access](https://atlas.mitre.org/tactics/AML.TA0000): **MITRE ATLAS**
+1. [Proof Pudding (CVE-2019-20634)](https://avidml.org/database/avid-2023-v009/) **AVID** (`moohax` & `monoxgas`)
+2. [arXiv:2403.06634 Stealing Part of a Production Language Model](https://arxiv.org/abs/2403.06634) **arXiv**
+3. [Runaway LLaMA | How Meta's LLaMA NLP model leaked](https://www.deeplearning.ai/the-batch/how-metas-llama-nlp-model-leaked/): **Deep Learning Blog**
 4. [I Know What You See:](https://arxiv.org/pdf/1803.05847.pdf): **Arxiv White Paper**
-6. [A Comprehensive Defense Framework Against Model Extraction Attacks](https://ieeexplore.ieee.org/document/10080996): **IEEE**
-7. [Alpaca: A Strong, Replicable Instruction-Following Model](https://crfm.stanford.edu/2023/03/13/alpaca.html): **Stanford Center on Research for Foundation Models (CRFM)**
-8. [How Watermarking Can Help Mitigate The Potential Risks Of LLMs?](https://www.kdnuggets.com/2023/03/watermarking-help-mitigate-potential-risks-llms.html): **KD Nuggets**
-9.  [Securing AI Model Weights Preventing Theft and Misuse of Frontier Models](https://www.rand.org/content/dam/rand/pubs/research_reports/RRA2800/RRA2849-1/RAND_RRA2849-1.pdf)
-10. [Sponge Examples: Energy-Latency Attacks on Neural Networks: Arxiv White Paper](https://arxiv.org/abs/2006.03463) **arXiv**
-11. [Sourcegraph Security Incident on API Limits Manipulation and DoS Attack](https://about.sourcegraph.com/blog/security-update-august-2023) **Sourcegraph**
+5. [A Comprehensive Defense Framework Against Model Extraction Attacks](https://ieeexplore.ieee.org/document/10080996): **IEEE**
+6. [Alpaca: A Strong, Replicable Instruction-Following Model](https://crfm.stanford.edu/2023/03/13/alpaca.html): **Stanford Center on Research for Foundation Models (CRFM)**
+7. [How Watermarking Can Help Mitigate The Potential Risks Of LLMs?](https://www.kdnuggets.com/2023/03/watermarking-help-mitigate-potential-risks-llms.html): **KD Nuggets**
+8. [Securing AI Model Weights Preventing Theft and Misuse of Frontier Models](https://www.rand.org/content/dam/rand/pubs/research_reports/RRA2800/RRA2849-1/RAND_RRA2849-1.pdf)
+9. [Sponge Examples: Energy-Latency Attacks on Neural Networks: Arxiv White Paper](https://arxiv.org/abs/2006.03463) **arXiv**
+10. [Sourcegraph Security Incident on API Limits Manipulation and DoS Attack](https://about.sourcegraph.com/blog/security-update-august-2023) **Sourcegraph**
 
 ### Related Frameworks and Taxonomies
 
 Refer to this section for comprehensive information, scenarios strategies relating to infrastructure deployment, applied environment controls and other best practices.
 
-- [NIST SP 800-53](https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-53r5.pdf) - **NIST Security and Privacy Controls for Information Systems and Organizations**
 - [MITRE CWE-400: Uncontrolled Resource Consumption](https://cwe.mitre.org/data/definitions/400.html) **MITRE Common Weakness Enumeration**
 - [AML.TA0000 ML Model Access: Mitre ATLAS](https://atlas.mitre.org/tactics/AML.TA0000) & [AML.T0024 Exfiltration via ML Inference API](https://atlas.mitre.org/techniques/AML.T0024) **MITRE ATLAS**
 - [OWASP Machine Learning Security Top Ten - ML05:2023 Model Theft](https://owasp.org/www-project-machine-learning-security-top-10/docs/ML05_2023-Model_Theft.html) **OWASP ML Top 10**