The Top 10 Large Language Models Security Risks

Understanding the Top 10 Security Risks Associated with Large Language Models (LLMs)

Introduction

Large Language Models (LLMs) have revolutionized the field of artificial intelligence and natural language processing, but with great power comes great responsibility. As LLMs become increasingly prevalent, it’s essential to understand the potential security risks they pose.

In light of OWASP’s recent announcement of the OWASP Top 10 Risk for Large Language Model Applications, this article aims to explore my perspective on the top 10 security risks associated with LLMs. I am eager to compare and contrast these risks with the ones OWASP will publish.

Cybersec.Cafè Top 10 Large Language Models Security Risks

1. Jailbreaking: Bypassing the security measures of an LLM to gain unauthorized control and exploit it for malicious purposes.
2. Prompt injection: Crafting prompts to influence the model’s output, which can lead to biased, offensive, or harmful text generation.
3. Second-order injections: Advanced prompt injection techniques, where the prompt itself is generated by an LLM, making it harder to detect and prevent attacks. Note: I’m not considering cross-content injections (a type of prompt injection where the prompt is generated in one context and then used to generate text in another context – this can be used to generate text that is relevant to the first context but harmful in the second context) as I consider still as in between of both risk 2 and 3
4. Data poisoning: Injecting malicious data into the training dataset, resulting in biased or harmful outputs. Rigorous validation and monitoring are crucial to mitigate this risk. This is actually a Machine Learning (ML) risk that extend to LLMs being that their training is ML based.
5. Misinformation: Unintentional contribution to the spread of misinformation or support for creating misinformation campaigns.
6. Malicious content generation: Misusing LLMs to generate persuasive or believable text for phishing or social engineering attacks.
7. Weaponization: Misusing LLMs to support coding malware or potentially even for malware detection evasion (still a theoretical threat) by generating malware code that evades traditional endpoint detection and response scanners. For example, an LLM could be used to generate malware code that is not detected by traditional Endpoint Detection and Response scanners as the code is generated by an LLM that provides it via API.
8. LLM-delivered attacks: Using LLMs to deceive users and obtain sensitive information or launch cyber attacks. For example, an LLM could be used to ask a user for sensitive information such as their passwords or credit card number.
9. Abuse of vertical LLM APIs: Exploiting LLMs for purposes outside their intended use cases, potentially undermining the intended business model.
10. Privacy: LLMs are trained on massive datasets that contain also personal information, raising privacy concerns if the models generate text like the confidential data it was trained from. This happens for instance with Inference Attacks or Model Inversion Attacks these attacks attempt to infer or recreate information about the training data from the outputs of an ML model.

Some other thoughts

• Organizations have their specific risks that are not necessarily the same of my Top 10.
• OpenAI’s LLMs are not currently subject to any restrictions on their use. This means that it might be more complex to mitigate any risk at all.
• Of course also the ML risks are part of the LLM ones

Conclusion

While the risks associated with LLMs may seem challenging, we don’t know yet if they are insurmountable. As of today, we still lack comprehensive solutions to mitigate most of these risks compared to other security domains like applications and mobile devices. Additionally, due to the “black box” nature of LLMs, understanding their inner workings presents challenges in determining the appropriate security measures to adopt. Furthermore, regulatory frameworks surrounding LLM use are still evolving, as discussed in my geopolitical analysis of the ChatGPT block in Italy.

LLM security contains a multitude of unknown unknowns, and it necessitates further research and mitigation strategies to effectively safeguard against these risks. Awareness serves as the critical first step towards achieving effective cybersecurity if it will be ever possible to reach it.

Recommended Readings

To delve deeper into the topic, I recommend reading the following insightful resources:

• https://www.wired.com/story/chatgpt-jailbreak-generative-ai-hacking/
• https://themathcompany.com/blog/data-poisoning-and-its-impact-on-the-ai-ecosystem
• https://spectrum.ieee.org/ai-cybersecurity-data-poisoning
• https://www.semianalysis.com/p/google-we-have-no-moat-and-neither
• https://ambcrypto.com/heres-how-to-jailbreak-chatgpt-with-the-top-4-methods-5/
• https://www.techopedia.com/what-is-jailbreaking-in-ai-models-like-chatgpt
• https://www.theregister.com/2023/04/26/simon_willison_prompt_injection/
• https://blogs.itemis.com/en/model-attacks-exploits-and-vulnerabilities
• https://research.nccgroup.com/2022/12/05/exploring-prompt-injection-attacks/
• https://hiddenlayer.com/research/the-dark-side-of-large-language-models/
• https://hiddenlayer.com/research/the-dark-side-of-large-language-models-2/
• https://embracethered.com/blog/posts/2023/ai-injections-direct-and-indirect-prompt-injection-basics/
• https://embracethered.com/blog/posts/2023/ai-injections-threats-context-matters/
• https://www.mufeedvh.com/llm-security/