The integration of automated red-teaming algorithms and continuously updated prompt injection attack repository represents a programmatic, robust, and adaptive approach to safeguarding large language models (LLMs).

TLDR The recent buzz around OpenAI's "Q* Project" has brought Q-Learning, a cornerstone of AI innovation since 1992, back into the limelight. But what exactly is Q-learning, and why does it matter in today's tech landscape?

TL:DR A new paper from researchers at Saarland University and CISPA discussed "indirect prompt injection", through which attackers could strategically place malicious instructions into sources that are likely to be ingested by the model at inference time. If retrieved, these poisoned prompts can then indirectly control the LLM and manipulate its behavior without any direct access.

TL:DR Research reveals fine turning breaks A.I. chatbot guardrails, showing risks as capabilities grow.

TL:DR New research reveals techniques to circumvent leading AI chatbots' safety guardrails, tricking them into generating harmful content. As chatbots proliferate, technical fixes alone cannot ensure safety.

TL:DR User-designed AI models have a major vulnerability - they can be "jailbroken" by hackers to steal sensitive information.

TL:DR A recent study by Northwestern University and security research company Coderrect Inc. have brought to light a critical vulnerability in Custom GPTs, a wake-up call for the AI community, emphasizing the paramount importance of Large Language Model (LLM) safety.