Fixed numbering in Prompt Injection (#660)

Signed-off-by: Talesh Seeparsan <[email protected]>

Author: talesh

2_0_vulns/LLM01_PromptInjection.md

@@ -75,14 +75,14 @@ Prompt injection vulnerabilities are possible due to the nature of generative AI
 4. [Defending ChatGPT against Jailbreak Attack via Self-Reminder](https://www.researchsquare.com/article/rs-2873090/v1) **Research Square**
 5. [Prompt Injection attack against LLM-integrated Applications](https://arxiv.org/abs/2306.05499) **Cornell University**
 6. [Inject My PDF: Prompt Injection for your Resume](https://kai-greshake.de/posts/inject-my-pdf) **Kai Greshake**
-8. [Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://arxiv.org/pdf/2302.12173.pdf) **Cornell University**
-9. [Threat Modeling LLM Applications](https://aivillage.org/large%20language%20models/threat-modeling-llm/) **AI Village**
-10. [Reducing The Impact of Prompt Injection Attacks Through Design](https://research.kudelskisecurity.com/2023/05/25/reducing-the-impact-of-prompt-injection-attacks-through-design/) **Kudelski Security**
-11. [Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (nist.gov)](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2023.pdf)
-12. [2407.07403 A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends (arxiv.org)](https://arxiv.org/abs/2407.07403)
-13. [Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks](https://ieeexplore.ieee.org/document/10579515)
-14. [Universal and Transferable Adversarial Attacks on Aligned Language Models (arxiv.org)](https://arxiv.org/abs/2307.15043)
-15. [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy (arxiv.org)](https://arxiv.org/abs/2307.00691)
+7. [Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://arxiv.org/pdf/2302.12173.pdf) **Cornell University**
+8. [Threat Modeling LLM Applications](https://aivillage.org/large%20language%20models/threat-modeling-llm/) **AI Village**
+9. [Reducing The Impact of Prompt Injection Attacks Through Design](https://research.kudelskisecurity.com/2023/05/25/reducing-the-impact-of-prompt-injection-attacks-through-design/) **Kudelski Security**
+10. [Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (nist.gov)](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2023.pdf)
+11. [2407.07403 A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends (arxiv.org)](https://arxiv.org/abs/2407.07403)
+12. [Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks](https://ieeexplore.ieee.org/document/10579515)
+13. [Universal and Transferable Adversarial Attacks on Aligned Language Models (arxiv.org)](https://arxiv.org/abs/2307.15043)
+14. [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy (arxiv.org)](https://arxiv.org/abs/2307.00691)
 
 ### Related Frameworks and Taxonomies