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Last 7 days · 67 items

2026-08-25 Tue
Truth Lies Deep: Countering Semantic Camouflage via Latent Intent VerificationarXiv cs.AI1 minAISecurity

arXiv:2608.20378v1 Announce Type: new Abstract: Safety alignment in Large Language Models (LLMs) is often superficial, relying on refusal mechanisms that trigger only at the final stages of generation without erasing the foundational knowledge of harmful concepts acquired during pretraining. This study demonstrates that this architectural disconnect leaves models vulnerable to Semantic Camouflage -- adversarial attacks that wrap harmful intent in benign narrative contexts (e.g., creative writing

De-Googled GrapheneOS is coming to Motorola’s foldables next yearThe Verge1 minOpen SourceSecurity

GrapheneOS, an open source version of Android that prioritizes security and privacy, has detailed its plans for supporting Motorola smartphones. Official support is set to arrive next year, starting with traditional flagships, before rolling out to Motorola's foldable phones and perhaps cheaper models, eventually. In a Mastodon thread, the GrapheneOS Foundation announced that it will […]

2026-08-24 Mon
2026-08-23 Sun
Jump in bot trafficreddit1 minSecurityCloud & Infra

Has anyone else noticed an increase in scanners/bots in the past ~month? For the past couple years I've had 2-3k hits a day from bots but lately there has been a steady increase in traffic looking mostly for php files. What I find strange is how much of this traffic is coming from MS and Google IPs. Do they not have any kind of monitoring on their cloud services? Having thousands of requests spamming every IP that responds should raise some flags. 20.24.67.246 Hong Kong Hong Kong Microsoft Corpo

2026-08-22 Sat
Anthropic Brings Claude Mythos 5 to Claude Security: Enterprise Teams Get Frontier Vulnerability Scanning Without Direct Model AccessMarkTechPost4 minAISecurity

Anthropic has moved its most cyber-capable model into a product security teams can switch on themselves. Claude Security scans now run on Claude Mythos 5, in public beta for Claude Enterprise customers with no separate model add-on. The scan connects to a GitHub repository, traces data flows across files, and returns findings with a CWE category, confidence and severity ratings, and a suggested patch. The design point is packaging: users receive a scan result rather than a prompt box, so the mod

2026-08-21 Fri
Safety Alignment Illusion: The Cross-Lingual Safety Gap in LLMsarXiv cs.AI1 minAISecurity

arXiv:2608.18131v1 Announce Type: new Abstract: Current safety alignment training for Large Language Models (LLMs) are heavily English-centric. When such safety filters fail for non-English languages, the consequences are immediate and user-facing: voice assistants and spoken dialogue systems may produce stereotype-reinforcing outputs, bypassing the standard English-focused safety alignments and propagating harmful bias to non-English speaking communities. For spoken language technologies deploy

2026-08-20 Thu
Fool's Gold: Defensive Deception Against Safety-Removal Attacks on Open-Weight ModelsarXiv cs.AI1 minAISecurity

arXiv:2608.17202v1 Announce Type: new Abstract: Safety alignment in open-weight language models is trivially removable: abliteration projects a refusal-mediating direction out of the weights in minutes, and no release-time defense we are aware of prevents it durably. What cannot be prevented can be deceived. Our defense, decoy hardening ("Fool's Gold"), concedes the refusal strip and poisons its payoff: once refusal is stripped, most answers to hazardous operational requests are confident, fluen

Benchmarking the Benchmarks: Evaluating Automated Safety Benchmarks for Small Language ModelsarXiv cs.AI1 minAISecurity

arXiv:2608.17183v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly deployed in resource-constrained, privacy-sensitive settings, where safety and bias failures can cause security and societal risks. However, existing AI safety\slash security\slash compliance benchmarks are designed for large language models that may not transfer reliably to SLMs. We therefore ask: Can these benchmarks effectively and reliably evaluate SLMs? To answer this question, we conduct a large-s

DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimizationarXiv cs.AI1 minAISecurity

arXiv:2608.17067v1 Announce Type: new Abstract: As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, further exacerbated by red-teaming adversarial attacks. Existing defenses predominantly operate under white-box assumptions, relying on text encoder optimization, weight editing, or inference-time intervention, and fundamentally cannot scale to proprietary models. Black-box alt

HarmProfile: Characterizing Harmful Distributions in Frontier LLMshf_papers1 minAISecurity

Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analysis. Consequently, little is known about the harmful outputs produced during model misbehavior, partly because large-scale, high-quality collections of frontier-LLM misbehavior are difficult to obtain. To address this gap, we introduce HarmProfile, a content-centric benchmark dataset that collects model misbehavior across diverse harm categories and

2026-08-19 Wed
OpenAI lays out new security changes after its AI hacked Hugging FaceThe Verge2 minSecurityAI

OpenAI is announcing security updates following the July news that its AI broke out of a sandboxed environment and accidentally hacked Hugging Face, including improvements to its research environments, monitoring, and alignment techniques. The company had already put the brakes on a new model, Astra, that it thinks could have "critical" cybersecurity capabilities, and the […]

Ventor-QTest: Threat-Model-Driven Verification of Vendor-Hosted LLM APIshf_papers1 minAISecurity

As large language models become increasingly widespread, third-party providers that deploy open-weight models have become an important part of the ecosystem. Auditing the quality of their inference APIs is therefore an open problem. We formalize hosted model routing as a stochastic process and propose \textbf{Ventor-QTest}, a composite black-box audit that requires no probability information from the target API. Its repeated-request component sends each frozen constrained context to the target m

Position: AI Lock-In Is in Progress, and We Must Be PreparedarXiv cs.AI1 minAISecurity

arXiv:2608.14565v1 Announce Type: new Abstract: AI safety research has mainly focused on two areas: technical alignment (ensuring AI systems produce human-aligned outputs) and the regulation of generative AI's societal impacts (including unemployment risk and labor market disruption). However, an equally important dimension remains underexplored: the risk inherent in dependence on AI systems themselves. In this position paper, we argue that AI safety research should address AI Lock-In, the pheno

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