Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Shana Dacres-Lawrence explains the complex ...
Your LLM-based systems are at risk of being attacked to access business data, gain personal advantage, or exploit tools to the same ends. Everything you put in the system prompt is public data.
Indirect prompt injection represents a more insidious threat: malicious instructions embedded in content the LLM retrieves ...
The OWASP Top 10 for LLM Applications is the most widely referenced framework for understanding these risks. First released in 2023, OWASP updated the list in late 2024 to reflect real-world incidents ...
We’ve explored how prompt injections exploit the fundamental architecture of LLMs. So, how do we defend against threats that ...
In this article, I would like to engage the reader in a thought experiment. I am going to argue that in the not-so-distant future, a certain type of prompt injection attack will be effectively ...
LLM-powered applications are rapidly expanding the enterprise attack surface — but not in entirely new ways. At their core, these systems still rely on APIs. What’s changed is how those APIs are used.
Bing added a new guideline to its Bing Webmaster Guidelines named Prompt Injection. A prompt injection is a type of cyberattack against large language models (LLMs). Hackers disguise malicious inputs ...
"Prompt injection attacks" are the primary threat among the top ten cybersecurity risks associated with large language models (LLMs) says Chuan-Te Ho, the president of The National Institute of Cyber ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results