Here's your daily roundup of the most relevant AI and ML news for February 06, 2026. Today's digest includes 2 security-focused stories. We're also covering 6 research developments. Click through to read the full articles from our curated sources.
Security & Safety
1. Claude Opus 4.6 vs. GPT-5.3-Codex: AI Model Showdown
Article URL: https://badlucksbane.com/posts/claude-opus-4-6-vs-gpt-5-3-codex-the-ai-model-showdown.html Comments URL: https://news.ycombinator.com/item?id=46918900 Points: 1
Comments: 0
Source: Hacker News - ML Security | 1 hours ago
2. Claude Opus 4.6 Finds 500+ High-Severity Flaws Across Major Open-Source Libraries
Artificial intelligence (AI) company Anthropic revealed that its latest large language model (LLM), Claude Opus 4.6, has found more than 500 previously unknown high-severity security flaws in open-source libraries, including Ghostscript, OpenSC, and CGIF. Claude Opus 4.6, which was launched Thurs...
Source: The Hacker News (Security) | 17 hours ago
Research & Papers
3. Learning to Inject: Automated Prompt Injection via Reinforcement Learning
arXiv:2602.05746v1 Announce Type: new Abstract: Prompt injection is one of the most critical vulnerabilities in LLM agents; yet, effective automated attacks remain largely unexplored from an optimization perspective. Existing methods heavily depend on human red-teamers and hand-crafted prompts, ...
Source: arXiv - Machine Learning | 18 hours ago
4. STACK: Adversarial Attacks on LLM Safeguard Pipelines
arXiv:2506.24068v3 Announce Type: replace-cross Abstract: Frontier AI developers are relying on layers of safeguards to protect against catastrophic misuse of AI systems. Anthropic and OpenAI guard their latest Opus 4 model and GPT-5 models using such defense pipelines, and other frontier develo...
Source: arXiv - AI | 18 hours ago
5. Phantom Transfer: Data-level Defences are Insufficient Against Data Poisoning
arXiv:2602.04899v1 Announce Type: cross Abstract: We present a data poisoning attack -- Phantom Transfer -- with the property that, even if you know precisely how the poison was placed into an otherwise benign dataset, you cannot filter it out. We achieve this by modifying subliminal learning to...
Source: arXiv - AI | 18 hours ago
6. Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning
arXiv:2602.04998v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is the prevailing approach for efficient large language model (LLM) fine-tuning. Building on this paradigm, recent studies have proposed alternative initialization strategies and architectural modifications, reporting sub...
Source: arXiv - Machine Learning | 18 hours ago
7. ALIVE: Awakening LLM Reasoning via Adversarial Learning and Instructive Verbal Evaluation
arXiv:2602.05472v1 Announce Type: new Abstract: The quest for expert-level reasoning in Large Language Models (LLMs) has been hampered by a persistent \textit{reward bottleneck}: traditional reinforcement learning (RL) relies on scalar rewards that are \textbf{costly} to scale, \textbf{brittle} ...
Source: arXiv - AI | 18 hours ago
8. A Causal Perspective for Enhancing Jailbreak Attack and Defense
arXiv:2602.04893v1 Announce Type: new Abstract: Uncovering the mechanisms behind "jailbreaks" in large language models (LLMs) is crucial for enhancing their safety and reliability, yet these mechanisms remain poorly understood. Existing studies predominantly analyze jailbreak prompts by probing ...
Source: arXiv - Machine Learning | 18 hours ago
About This Digest
This digest is automatically curated from leading AI and tech news sources, filtered for relevance to AI security and the ML ecosystem. Stories are scored and ranked based on their relevance to model security, supply chain safety, and the broader AI landscape.
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