
File photo · CC BY-SA 3.0
Google AI gets cagey when asked who won the 2020 elections
But the AI responses get more forthright when asked outside the US.
Details
AI context
AI-assisted context, not publisher reporting. It may contain errors.
Updated 2026-10-29 04:40 UTC
Follow via RSSBrowser storage is unavailable. Saves and preferences last only until this page is closed or reloaded.
123 headlines
Try another search or restore all stories in this section.

But the AI responses get more forthright when asked outside the US.
AI-assisted context, not publisher reporting. It may contain errors.

AI-assisted context, not publisher reporting. It may contain errors.

The Echo Studio with Home Camera essentially equips Alexa+ with eyes to enable ‘visual intelligence’ features – but there’s a kill switch.
In light of Meta's new terms that will allow it to scrape users' data for AI, people in the EU and UK have the option to "object" to certain uses of their information, for now.
Cybersecurity startup Cathedral used AI agents to break into Anthropic's internal systems through third-party software as part of a bug bounty program. Anthropic confirmed access to sales, HR, and IT…
With OpenAI Daybreak, Check Point speeds up the research behind new security protections, enabling its analysts to turn vulnerability intelligence into customer defenses faster.
We introduce Heads of Security (HoS), a prompt injection defense that enforces instruction-data separation via a small set of learned per-head attention gates. HoS learns how each head should redistribute attention between trusted and…
Model quantization has become a fundamental technique for enabling the efficient deployment of large-scale deep neural networks (DNNs) in data-intensive applications by reducing model size. Despite its widespread adoption, the impact…
AI chatbot platforms have become one of the most popular web interfaces users interact with online, routinely receiving personal and intimate information shared under an expectation of privacy. Yet, on the web application side, these…
Prior work framed the envelope layer as the channel through which malicious content enters an agent, and established it as a defense surface independent of the LLM layer and the agent layer; however, that work only tested the envelope…
Agentic Entity Resolution (ER) systems iteratively plan actions, retrieve evidence, revise hypotheses, and propose graph mutations. An erroneous identity hypothesis may condition later retrieval and produce apparent confirmation. This…
Proprietary language models and their reasoning traces are vulnerable to API-based distillation, where an adversary trains a student model on the teacher model's outputs. To obtain evidence of distillation, model providers can…
Multi-relational graph detectors learn from clean data how much to trust each relation, but an adversary pays a different price to manipulate each relation and each action; taking such costs as given, we ask when using a relation is…
Automotive cybersecurity commonly assumes benign end-product manufacturers and design authorities, focusing on supply-chain threats, external attacks, and compromised network participants observable through misbehaviour. This…
Audit-based certification bounds the violation rate of an LLM agent by auditing a random fraction $\varepsilon$ of actions and reweighting the audited labels; its validity is meant to rest only on the defender's audit coin. We show…
Despite significant advances in computer vision, semantic segmentation models remain vulnerable to backdoor attacks, posing a security risk. These attacks redirect pixels of a chosen victim class to an attacker-specified target class…
As smart organizations increasingly rely on technologies like AI, big data, and IoT, securing these systems is crucial. This research introduces a dynamic multi-factor authentication (MFA) system within the Zero Trust Architecture…
Payment channel hubs (PCHs) enable private cross-chain payments, but existing protocols remain difficult to extend across heterogeneous blockchains. Their payment flows often depend on how particular blockchains represent…
Reliable evaluation is a critical bottleneck in privacy red-teaming of large language models (LLMs): determining whether a jailbreak response actually reveals personal information can be difficult when disclosures are indirect…
Security pipelines often fuse several data sources and protect a downstream model with a cross-source anomaly detector. Such detectors are usually judged by their clean false-positive rate and by how well they catch random corruption…
While few-step distilled text-to-image models have gained traction due to their fast generation times, the speed-up implications on their unsafe generation rates remain unknown. We uncover an inherent tension between speed and safety…
Third-party community-based bots are central to how social platforms such as Discord work. They moderate content, manage roles, play music, and support AI-assisted conversations across millions of servers. Yet when a bot is installed…
Hardware malware detectors (HMDs) classify hardware performance-counter measurements to identify malicious execution. Faults in their decision logic or parameters can weaken detection and allow malware to be classified as benign. We…
Global narrative streams exhibit noisy, fragmented, and asynchronous amplification bursts, making direct waveform comparison unreliable for recurrence analysis. This paper introduces Peak-DNA, a framework for identifying recurrent…