Safety
A fundamental flaw leaves LLMs strikingly vulnerable to attack
It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue in a paper presented at the International Conferen
It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue in a paper presented at the International Conference on Machine Learning, a top AI conference, this month. The claim has huge implications for the safety of this technology, which…
Related
- PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training
- Latent Instruction Representation Alignment: defending against jailbreaks, backdoors and undesired knowledge in LLMs
- Re-Triggering Safeguards within LLMs for Jailbreak Detection
- Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models
- Quantifying LLM Safety Degradation Under Repeated Attacks Using Survival Analysis
Source: MIT Tech Review | 2026-07-30