Hugging Face Trending Papers
SEMA-Auto: Sighted and Efficient Multi-Turn Attack with Autonomy
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv Computation and Language
Oct 27
SEMA-Auto: Sighted and Efficient Multi-Turn Attack with Autonomy
arXiv:2610.35743v1 Announce Type: new Abstract: Existing jailbreak attackers fall into two classes: static planners fix their single- or multi-turn adversarial plans in advance, remaining simple but...
arXiv Machine Learning
Sep 14
SaFeR-Steer: Evolving Multi-Turn MLLMs via Synthetic Bootstrapping and Feedback Dynamics
SaFeR-Steer is a progressive multi‑turn alignment framework that uses staged synthetic bootstrapping and tutor‑in‑the‑loop GRPO to train a single student model against adaptive, on‑policy attacks. It introduces Trajectory‑Consistent Summative Reward (TCSR) to ensure that low‑quality turns impact the overall trajectory reward. The authors release the STEER dataset and demonstrate that SaFeR‑Steer significantly improves safety and helpfulness on both single‑turn and multi‑turn benchmarks for Qwen2.5‑VL models.
arXiv AI
Oct 26
$M^3$: A Multi-LLM System for Multi-Turn Jailbreaking as a Markov Decision Process
$M^3$ is an automated red‑teaming framework that models multi‑turn jailbreaking as a finite‑horizon Markov Decision Process. It lets an attacker LLM interact with a target LLM while using a state LLM to maintain a compact internal state. Across two benchmarks and six target models, $M^3$ outperforms existing methods, boosting attack success rates by up to 53% at five turns and improving consistency and risk escalation metrics.
arXiv AI
Aug 18
TRACE: Trajectory Aware Reasoning for Multi-Turn Adversarial Conversation Evaluation
arXiv:2608. 15594v1 Announce Type: new Abstract: Multi-turn jailbreak attacks have emerged as a critical safety threat to LLMs, as harmful objectives are decomposed across a sequence of apparently benign turns to bypass guardrails.
arXiv AI
Jul 21
Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security
arXiv:2607. 18063v1 Announce Type: cross Abstract: LLM-based agents process external content, exposing them to prompt injection and multi-turn manipulation.
arXiv Machine Learning
Jun 26
Jailbreaking for the Average Jane: Choosing Optimal Jailbreaks via Bandit Algorithms for Automatically Enhanced Queries
arXiv:2606. 26936v1 Announce Type: cross Abstract: With a profusion of jailbreaks for LLMs now widely known, a growing concern is that non-expert malicious actors ("the average Jane") could elicit actionable responses to malicious requests.