📮 Maildrop 21.01.25: Building robust and reliable LLMs: Defending against adversariality
The things to know about AI and cyber threats | Tools for the next generation of enterprises in the AI era
📮 Maildrop 21.01.25: Building robust and reliable LLMs: Defending against adversariality
Imagine a world where cyberattacks are not just prevented but predicted and neutralized before they occur.
This is the promise of defining how threats evolve using large language models (LLMs) to transform the cybersecurity landscape and improve online safety.
➤ More on this is discussed in the introduction of this new series.
In the rapidly evolving threat landscape, attackers constantly seek new ways to exploit vulnerabilities.
This applies not only to traditional software systems but also to the AI models that underpin your security infrastructure.
While powerful, LLMs are not immune to adversarial attacks —malicious manipulations designed to deceive or disrupt their operation.
These attacks can have significant consequences, potentially leading to false negatives (missed threats) or false positives (legitimate activity flagged as malicious).
What’s important to know?
↓↓ More below ↓↓