I’m thrilled to announce that in the next months I will be speaker to a couple of interesting events in Milan. The next one is the 12th of March and of course I’ll talk about AI Cybersecurity.
Back to the main news: in just a few days, I’ll be embarking on a series of captivating collaborations with some esteemed minds in the cybersecurity field in Cybersec.cafe and I’ll be guest of another blog that will be revealed in due time.
Buckle up, because we’re diving deep into valuable insights you won’t want to miss. While I can’t reveal all the surprises just yet, let me assure you that these partnerships will bring together diverse perspectives and a wealth of experience. We’ll be tackling some pressing issues in the world of cyber.
The next guest will be Fabrizio Cilli and he will discuss the 23andMe breach and its implications in terms of shared responsibility in cybersecurity – sorry I won’t disclose more as spoiler is a capital crime nowadays but trust me, you won’t want to miss this!
Stay tuned for further details future announcements.
See you soon!
P.S. Want to be the first to know when the collaborations kick off? Follow me on linkedin and keep an eye out for updates!
This corresponds to several risks in OWASP’s list: LLM03:2023 – Inadequate Sandboxing, LLM04:2023 – Unauthorized Code Execution, LLM05:2023 – SSRF Vulnerabilities, LLM08:2023 – Insufficient Access Controls, and LLM09:2023 – Improper Error Handling.
In my perspective, Jailbreaking refers to the process of gaining unauthorized access to and control over an LLM’s underlying systems or processes, while OWASP risks might pertain more to the system or application underpinning the LLM rather than the LLM itself. While jailbreaking could serve as an entry point for exploiting these OWASP risks, the mitigation strategies may not be fully effective in all cases.
By articulating these risks separately, OWASP’s approach might help define individual mitigation actions.
These risks directly align with OWASP’s LLM01:2023 – Prompt Injections, although OWASP’s category encompasses all forms of prompt injections.
4. Data Poisoning
This directly aligns with OWASP’s LLM10:2023 – Training Data Poisoning.
5. Misinformation
This risk somewhat corresponds to OWASP’s LLM06:2023 – Overreliance on LLM-generated Content, especially in scenarios where overreliance results in misinformation. However, OWASP’s category includes other potential issues, such as bias, making it more comprehensive.
6. Malicious content generation
This risk intersects with OWASP’s LLM07:2023 – Inadequate AI Alignment. The link might seem tenuous, but the principle remains that an LLM’s use case should not be creating malicious content.
7. Weaponization, 8. LLM-delivered attacks
These risks overlap with OWASP’s LLM04:2023 – Unauthorized Code Execution and LLM07:2023 – Inadequate AI Alignment. These risks underscore the potential for LLMs to be exploited for malicious purposes, be it coding malware or delivering attacks.
9. Abuse of vertical LLM APIs
This risk relates to OWASP’s LLM07:2023 – Inadequate AI Alignment and LLM08:2023 – Insufficient Access Controls. Poor AI alignment could potentially lead to misuse of the LLM, and similarly, poor access control could result in unauthorized actions.
10. Privacy and Data Leakage
This risk directly corresponds to OWASP’s LLM02:2023 – Data Leakage.
Conclusion
In creating this top 10 and comparing it with OWASP’s list, I observed that the key differences lie in the granularity and standardization of terminology.
The field of LLM security is still relatively nascent, and there is a noticeable need for standardization of terms. This comparison has shed light on this fact.
I hope that OWASP’s risk list will bring the critical security considerations for LLMs into sharper focus, laying a solid foundation for further discussions and the development of security measures in this rapidly evolving technology sphere.
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