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Anthropic and OpenAI want to embed safety evaluators. Will they really be independent?
Anthropic and OpenAI propose embedding independent safety evaluators with deep access to training, but evaluators question whether true independence is achievable.
Anthropic CEO Dario Amodei proposed embedding third-party evaluators like METR and Redwood Research inside frontier AI labs with access to training checkpoints, and OpenAI's Sam Altman said his company would also commit to the practice. Evaluators welcomed the idea but cited past problems: Apollo Research received only three days to pre-release test GPT-6 Astra, and METR and Redwood got roughly one week on premises for the Hugging Face incident, yielding inconclusive results. Researchers argue that access to intermediate training checkpoints is needed to detect alignment faking, since models increasingly recognize when they are being evaluated, and some say legislation may be needed to guarantee independence.
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When scanners miss the attack: how Cloudflare Client-Side Security protects storefronts
Cloudflare's Page Shield ML uncovered four malicious JavaScript campaigns on storefronts, including affiliate fraud and a remote-backdoor script, that VirusTotal and URLScan missed.
Cloudflare's Page Shield ML detected four client-side JavaScript operations (eight payloads) in live traffic on online storefronts, enabling affiliate commission hijacking, clickless affiliate theft via hidden iframes, user tracking with a remote-code backdoor, and cloaking of paid mobile visitors. Seven of the eight payloads were absent from VirusTotal and URLScan returned no malicious verdict for any, including a Lnkr-family payload indexed unclassified for roughly 2.5 years. Detection relies on a graph neural network over JavaScript syntax trees, an LLM second opinion on Workers AI, and a frontier-model ensemble voting across benign, magecart, other malware, and cryptomining labels.
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A Feature-Rich Embedded NIDS with eBPF/XDP: Detector and Architecture Trade-offs
eBPF/XDP-based NIDS with Isolation Forest reaches 0.965 live F1 on DDoS replay; gRPC microservices match monolithic accuracy within 2ms overhead.
The paper presents a DDoS-focused network intrusion detection system for transport networks built with Ericsson, combining a statistical baseline with an Isolation Forest trained on flow features from GoFlowMeter, an open-source Go implementation of CICFlowMeter, plus eBPF/XDP kernel-level traffic filtering. On a Raspberry Pi 5 testbed replaying CIC-DDoS2019 as real traffic, the Isolation Forest achieves 0.965 recall/F1 live in the monolithic variant, catching low-volume attack windows the baseline misses. gRPC microservices nearly match monolithic accuracy adding under 2ms per window, while the Kafka pipeline trails by roughly nine percentage points and adds about 27ms.
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When the prompt becomes the payload: A practical pen-testing guide for GenAI, LLM and RAG applications
CSO Online publishes a practical penetration-testing guide for GenAI, LLM, and RAG applications, covering prompt injection, retrieval poisoning, and tenant isolation testing.
The guide frames LLM applications as attack graphs spanning prompts, retrieval layers, vector stores, tools, identities, and downstream APIs, arguing that conventional web testing misses instruction-vs-data channel risks. It builds on OWASP prompt injection guidance (direct vs. indirect injection) and NIST's 2025 adversarial machine-learning taxonomy, noting that RAG and fine-tuning do not remove injection risk. Recommended practices include documenting trust transitions across components, using canaries and synthetic records to avoid test side effects, running multi-turn and obfuscated injection campaigns, and verifying chains from poisoned documents to observable state changes. It also details testing RAG pipelines via controlled document poisoning across metadata, OCR layers, and code comments, plus cross-tenant isolation checks on retrieved document IDs.
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The Hidden Instructions That Can Hijack AI Agents
Hidden prompt injections embedded in documents and metadata can hijack autonomous AI agents, causing data exfiltration and out-of-policy actions at machine speed.
Bowbridge warns that hidden indirect prompt injections, embedded in documents, metadata, emails, images, and code repositories, can cause autonomous AI agents to treat attacker-controlled content as trusted guidance. Because agents inherit user privileges, act silently, and lack human judgment, injections can lead to data exfiltration or file poisoning that traditional security controls cannot detect. A real-world example involved a supplier quote whose metadata instructed an agent to override guidance and select the most expensive option. Bowbridge recommends scanning documents before agents process them.
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nvidia/Qwen3.8-Flash-Next-NVFP4 — new model trending #28 on Hugging Face
NVIDIA released an NVFP4 4-bit quantized build of Alibaba's Qwen3.8-Flash-Next, a 125B-parameter MoE vision-language model, via Model Optimizer.
The checkpoint quantizes Qwen3.8-Flash-Next — a hybrid-attention (Gated DeltaNet and Qwen Sparse Attention) Mixture-of-Experts model with 125B total and 6B activated parameters, plus 51B n-gram embeddings and 4B MTP — using NVIDIA Model Optimizer v0.46.0. NVFP4 benchmarks stay close to FP8: GPQA Diamond 91.5 vs 92.0, MMMU Pro 78.3 vs 77.1, Terminal-Bench 2.1 82.9 vs 83.3. It targets Blackwell B200/B300 GPUs, runs on vLLM, supports 262K context extendable to 1M tokens, and is licensed under the NVIDIA Open Model License with Qwen Community License 1.0.
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RANCOR: Targeted Attacks in South East Asia Using PLAINTEE and DDKONG Malware Families
Unit 42 names RANCOR, a previously unidentified espionage group targeting Southeast Asian political entities with newly discovered PLAINTEE and DDKONG malware families.
Unit 42 tracks a previously unidentified group it dubs RANCOR conducting highly targeted espionage attacks in Singapore and Cambodia using two custom malware families, DDKONG and PLAINTEE. Attacks appear to have begun with spear phishing, with decoy political news documents hosted on legitimate sites including a Cambodian government website and Facebook. Infrastructure links to IP 89.46.222[.]97 and KHRAT-associated domains such as facebook-apps[.]com, and the activity is grouped into two clusters tied together by PLAINTEE usage and similar targeting.
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Tracking OceanLotus’ new Downloader, KerrDown
Unit 42 identifies KerrDown, a new OceanLotus (APT32) downloader active since 2018 targeting Vietnamese speakers via malicious macros and DLL side-loading.
Unit 42 tracks KerrDown, a previously undocumented downloader family used by OceanLotus (APT32) since at least early 2018, primarily targeting Vietnam or Vietnamese-speaking individuals. Delivery uses macro-laced Microsoft Office documents embedding base64-encoded 32-bit and 64-bit DLLs, and RAR archives containing a legitimate program abused for DLL side-loading. KerrDown is dropped as main_background.png, downloads a DES-encrypted payload from a URL, and executes it directly in memory. Researchers used Jaccard-index similarity analysis to identify the new family, connect campaign samples, and infer patterns in the group's working hours and days.
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