FIFA launch investigation into Argentina's shocking World Cup final post-match behaviour... as new footage shows what really sparked final-whistle brawl Trump's cringe World Cup cameo was a fitting finale to a tournament blighted by excess, greed and FIFA boss Gianni Infantino's twisted dreams'People hate her': KENNEDY's Hollywood mole unleashes on Angelina Jolie... as two more of her kids publicly humiliate dad Brat Pitt Car experts reveal the little-known stereotypes that are actually TRUE... What does your vehicle really say about you? No one's shocked Harry wants to reverse Megxit. Actress Mia Farrow, 81, learns the hard way that you'll never be woke enough for the transgender community MAGA-friendly Fox News star Maria Bartiromo hints she's losing patience with Trump by asking VERY pointed question to ex-White House aide Shakira fans marvel over her'ageless' appearance at World Cup final as her ex-husband watches on from the stands with their sons Mystery over what REALLY caused'explosive diarrhea' parasite as FDA admits lettuce that was first to blame was a'false positive' Ever since I went on Vyvanse for ADHD, my marriage has felt different. A comment from my husband made me realise why women are calling it the'divorce drug': ASK JANA Scandal engulfs'it girl' fashion empire Set Active: Explosive claims about influencer founder's desperate fixation... 'inappropriate' texts that went ignored... and why employees wanted to stay on her'good side' Republican defies Trump to enter race to replace Lindsey Graham in the Senate... in move that could backfire on GOP Inside Luke Wilson's reclusive life with decades-younger girlfriend Kendall Yates: Friends reveal new details about their'secretive' romance after birth of their child... as unseen pictures of her emerge Hollywood's new youth elixir: Wealthy women are hooked on secret holy grail said to banish wrinkles and plump skin... but doctors warn of shock risk Inside the'pure evil' Ohio'House of Horrors': Forensic psychologists analyze every disturbing detail about the 16 children found living in squalor Bitter Argentina players turn their backs to Spain's World Cup trophy lift after violence marred winners' celebrations - as lip-reader reveals what furious star said to winning captain Rodri during brawl READ MORE: I worked for Apple. These secret iPhone tricks can save you from spending $1,000 on an upgrade... and the one fix that will make your device feel'like brand new' A billion-dollar milk company has been forced to halt production following a devastating cyberattack.

We present NN4SysBench, a benchmark suite for neural network verification that is composed of applications from the domain of computer systems. We call these neural networks for computer systems or NN4Sys. NN4Sys is booming: there are many proposals for using neural networks in computer systems--for example, databases, OSes, and networked systems--many of which are safety critical. Neural network verification is a technique to formally verify whether neural networks satisfy safety properties. We however observe that NN4Sys has some unique characteristics that today's verification tools overlook and have limited support. Therefore, this benchmark suite aims at bridging the gap between NN4Sys and the verification by using impactful NN4Sys applications as benchmarks to illustrate computer systems' unique challenges. We also build a compatible version of NN4SysBench, so that today's verifiers can also work on these benchmarks with approximately the same verification difficulties.

Neural Information Processing Systems http://nips.cc/

We present NN4SysBench, a benchmark suite for neural network verification that is composed of applications from the domain of computer systems. We call these neural networks for computer systems or NN4Sys . NN4Sys is booming: there are many proposals for using neural networks in computer systems--for example, databases, OSes, and networked systems--many of which are safety-critical. Neural network verification is a technique to formally verify whether neural networks satisfy safety properties.

JLR suppliers'face bankruptcy' due to hack crisis The past two weeks have been dreadful for Jaguar Land Rover (JLR), and the crisis at the car maker shows no sign of coming to an end. A cyber attack, which first came to light on 1 September, forced the manufacturer to shut down its computer systems and close production lines worldwide. Its factories in Solihull, Halewood, and Wolverhampton are expected to remain idle until at least Wednesday, as the company continues to assess the damage. JLR is thought to have lost at least £50m so far as a result of the stoppage. But experts say the most serious damage is being done to its network of suppliers, many of whom are small and medium sized businesses.

Large language model fine-tuning APIs enable widespread model customization, yet pose significant safety risks. Recent work shows that adversaries can exploit access to these APIs to bypass model safety mechanisms by encoding harmful content in seemingly harmless fine-tuning data, evading both human monitoring and standard content filters. We formalize the fine-tuning API defense problem, and introduce the Cipher Fine-tuning Robustness benchmark (CIFR), a benchmark for evaluating defense strategies' ability to retain model safety in the face of cipher-enabled attackers while achieving the desired level of fine-tuning functionality. We include diverse cipher encodings and families, with some kept exclusively in the test set to evaluate for generalization across unseen ciphers and cipher families. We then evaluate different defenses on the benchmark and train probe monitors on model internal activations from multiple fine-tunes. We show that probe monitors achieve over 99% detection accuracy, generalize to unseen cipher variants and families, and compare favorably to state-of-the-art monitoring approaches. We open-source CIFR and the code to reproduce our experiments to facilitate further research in this critical area. Code and data are available online https://github.com/JackYoustra/safe-finetuning-api

Neural Information Processing Systems http://nips.cc/

We present NN4SysBench, a benchmark suite for neural network verification that is composed of applications from the domain of computer systems. We call these neural networks for computer systems or NN4Sys. NN4Sys is booming: there are many proposals for using neural networks in computer systems--for example, databases, OSes, and networked systems--many of which are safety critical. Neural network verification is a technique to formally verify whether neural networks satisfy safety properties. We however observe that NN4Sys has some unique characteristics that today's verification tools overlook and have limited support.

Large language models (LLMs) have shown strong performance across natural language tasks, but remain vulnerable to backdoor attacks. Recent model editing-based approaches enable efficient backdoor injection by directly modifying parameters to map specific triggers to attacker-desired responses. However, these methods often suffer from safety fallback, where the model initially responds affirmatively but later reverts to refusals due to safety alignment. In this work, we propose DualEdit, a dual-objective model editing framework that jointly promotes affirmative outputs and suppresses refusal responses. To address two key challenges -- balancing the trade-off between affirmative promotion and refusal suppression, and handling the diversity of refusal expressions -- DualEdit introduces two complementary techniques. (1) Dynamic loss weighting calibrates the objective scale based on the pre-edited model to stabilize optimization. (2) Refusal value anchoring compresses the suppression target space by clustering representative refusal value vectors, reducing optimization conflict from overly diverse token sets. Experiments on safety-aligned LLMs show that DualEdit improves attack success by 9.98\% and reduces safety fallback rate by 10.88\% over baselines.