CoTGuard: Using Chain-Of-Thought Triggering for Copyright Protection in Multi-Agent LLM Systems

As large language models LLMs evolve into autonomous agents capable of collaborative reasoning and task execution, multi-agent LLM systems have emerged as a powerful paradigm for solving complex problems. However, these systems pose new challenges for copyright protection, particularly when...

SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning

Large Reasoning Models LRMs introduce a new generation paradigm of explicitly reasoning before answering, leading to remarkable improvements in complex tasks. However, they pose great safety risks against harmful queries and adversarial attacks. While recent mainstream safety efforts on LRMs,...

Robust and Efficient AI-Based Attack Recovery in Autonomous Drones

We introduce an autonomous attack recovery architecture to add common sense reasoning to plan a recovery action after an attack is detected. We outline use-cases of our architecture using drones, and then discuss how to implement this architecture efficiently and securely in edge devices...

AutoRAN: Weak-To-Strong Jailbreaking of Large Reasoning Models

This paper presents AutoRAN, the first automated, weak-to-strong jailbreak attack framework targeting large reasoning models LRMs. At its core, AutoRAN leverages a weak, less-aligned reasoning model to simulate the target model's high-level reasoning structures, generates narrative prompts, and...

GuardReasoner-VL: Safeguarding VLMs Via Reinforced Reasoning

To enhance the safety of VLMs, this paper introduces a novel reasoning-based VLM guard model dubbed GuardReasoner-VL. The core idea is to incentivize the guard model to deliberatively reason before making moderation decisions via online RL. First, we construct GuardReasoner-VLTrain, a reasoning...

DMind Benchmark: toward a Holistic Assessment of LLM Capabilities across the Web3 Domain

Large Language Models LLMs have achieved impressive performance in diverse natural language processing tasks, but specialized domains such as Web3 present new challenges and require more tailored evaluation. Despite the significant user base and capital flows in Web3, encompassing smart contracts...

Practical Reasoning Interruption Attacks on Reasoning Large Language Models

Reasoning large language models RLLMs have demonstrated outstanding performance across a variety of tasks, yet they also expose numerous security vulnerabilities. Most of these vulnerabilities have centered on the generation of unsafe content. However, recent work has identified a distinct...

System Prompt Poisoning: Persistent Attacks on Large Language Models beyond User Injection

Large language models LLMs have gained widespread adoption across diverse applications due to their impressive generative capabilities. Their plug-and-play nature enables both developers and end users to interact with these models through simple prompts. However, as LLMs become more integrated in...

DMRL: Data- and Model-Aware Reward Learning for Data Extraction

Large language models LLMs are inherently vulnerable to unintended privacy breaches. Consequently, systematic red-teaming research is essential for developing robust defense mechanisms. However, current data extraction methods suffer from several limitations: 1 rely on dataset duplicates...

The Steganographic Potentials of Language Models

The potential for large language models LLMs to hide messages within plain text steganography poses a challenge to detection and thwarting of unaligned AI agents, and undermines faithfulness of LLMs reasoning. We explore the steganographic capabilities of LLMs fine-tuned via reinforcement learnin...

The Automation Advantage in AI Red Teaming

This paper analyzes Large Language Model LLM security vulnerabilities based on data from Crucible, encompassing 214,271 attack attempts by 1,674 users across 30 LLM challenges. Our findings reveal automated approaches significantly outperform manual techniques 69.5% vs 47.6% success rate, despite...

CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenges

Large language models LLMs have demonstrated remarkable capabilities, especially the recent advancements in reasoning, such as o1 and o3, pushing the boundaries of AI. Despite these impressive achievements in mathematics and coding, the reasoning abilities of LLMs in domains requiring cryptograph...

Steering the CensorShip: Uncovering Representation Vectors for LLM "Thought" Control

Large language models LLMs have transformed the way we access information. These models are often tuned to refuse to comply with requests that are considered harmful and to produce responses that better align with the preferences of those who control the models. To understand how this "censorship...

Everything You Wanted to Know about LLM-Based Vulnerability Detection but Were Afraid to Ask

Large Language Models are a promising tool for automated vulnerability detection, thanks to their success in code generation and repair. However, despite widespread adoption, a critical question remains: Are LLMs truly effective at detecting real-world vulnerabilities? Current evaluations, which...

Trojans disguised as AI: Cybercriminals exploit DeepSeek’s popularity

Introduction Among the most significant events in the AI world in early 2025 was the release of DeepSeek-R1 – a powerful reasoning large language model LLM with open weights. It's available both for local use and as a free service. Since DeepSeek was the first service to offer access to a reasoni...

More Research Showing AI Breaking the Rules

These researchers had LLMs play chess against better opponents. When they couldn't win, they sometimes resorted to cheating. Researchers gave the models a seemingly impossible task: to win against Stockfish, which is one of the strongest chess engines in the world and a much better player than an...

CVE-2024-35820

Rejected reason: This CVE ID has been rejected or withdrawn by its CVE Numbering Authority...

RTX 安全漏洞

RTX is RTXteam's prototype translation reasoning tool. A security vulnerability exists in RTX TRAP v1.0. An attacker can spoof the server by manipulating the host header field in an HTTP request, causing the server to process the request with a malicious hostname...

forgekit-reasoning (0.5.0), haneru (0.0.0) +7 more potentially affected by CVE-2020-35876 via rio (0.9.4)

rio CARGO version 0.9.4 is affected by a known vulnerability. The following packages have a transitive dependency on rio and may be impacted: - forgekit-reasoning 0.5.0 - haneru 0.0.0 - marble 0.0.0 - mfio-rt 0.1.0 - pearl =0.4.0, =0.31.0, =0.39.0, =0.34.7-p1, =0.34.7-patched Source cves:...

forgekit-reasoning (0.5.0), haneru (0.0.0) +7 more potentially affected by CVE-2020-35876 via rio (0.9.4)

rio CARGO version 0.9.4 is affected by a known vulnerability. The following packages have a transitive dependency on rio and may be impacted: - forgekit-reasoning 0.5.0 - haneru 0.0.0 - marble 0.0.0 - mfio-rt 0.1.0 - pearl =0.4.0, =0.31.0, =0.39.0, =0.34.7-p1, =0.34.7-patched Source cves:...