Research on Defending and Exploiting LLMs via Jailbreak and Prompt-Manipulation Techniques

Recent research highlights how LLM jailbreak and prompt-manipulation attacks can bypass safety controls, especially in multi-turn conversations where adversaries gradually escalate requests to elicit harmful or policy-violating output. A proposed defense framework, HoneyTrap, aims to counter these attacks with a multi-agent approach that goes beyond static filtering or supervised fine-tuning by using adaptive, deceptive responses intended to slow attackers and deny actionable information rather than simply refusing requests.

Separately, technical analysis of the LLM input-processing pipeline (tokenization, embeddings, attention, and context-window behavior) explains why common guardrails like keyword filters can fail and how attackers can exploit architectural properties (including Query-Key-Value attention dynamics) to steer model behavior. The research describes common offensive techniques—prompt injection, jailbreaking, and adversarial suffixes—and frames them as practical risks for enterprise deployments, particularly public-facing chatbots and other systems where organizations cannot fully control user input.