Search: “rewriting”

6 stories in the last 24h

DeepSeek-V4.1 Flash is a 552B-parameter multimodal MoE model with 1M-token context achieving 4x KV cache compression for long-horizon agent workloads.

A detailed analysis of the DeepSeek-V4.1 Flash technical report describes a 552B-parameter multimodal mixture-of-experts model supporting contexts up to 1 million tokens. Its Causal Encoder-Decoder (CED) architecture activates 8B parameters during prefill and 16B during decode, and reportedly delivers about 420 tokens/s. Joint optimization of architecture (CSA2 cross-layer compression), FP4 KV cache precision, and deployment strategy cuts runtime KV cache to roughly 1/4 and persistent KV cache to about 1/8 of DeepSeek-V4-Flash at the same sequence length, targeting storage and bandwidth bottlenecks in long-horizon agent serving. The author notes all DeepSeek-V4 Pro models were taken offline following the release.

Hacker News · AI01:39 UTC · 2h agoModel release 4 sources HN 41↑ · 3 comments

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.

Cloudflare BlogSep 16, 20:06 UTC · 8h agoMalware in the wild

Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluationnew

Study shows radiology reporting-style variations in reference reports can flip rankings of chest X-ray report generation models; releases MIMIC-CXR-Ext-ReRef dataset.

The paper quantifies how variations in radiologists' reporting practices distort evaluation of radiology report generation (RRG) models, introducing a radiologist-informed taxonomy and the ReRef method for rewriting reference reports while preserving clinical meaning. On MIMIC-CXR with RadCliQ-v1, condensing normal-findings discussion caused Libra to drop from first to second while CheXOne rose from third to first among nine models. The authors release MIMIC-CXR-Ext-ReRef, a radiologist-validated dataset of 120 original/alternative reference pairs, arguing metrics conflate clinical correctness with stylistic conformity.

arXiv cs.AI / cs.LG / cs.CLSep 16, 17:28 UTC · 11h agoAI research

CASHEWS: Source Preprocessor for LLM-based Malicious Package Detectionnew

CASHEWS preprocessor boosts LLM-based malicious npm package detection, raising coverage to 98.8-100% and cutting false negatives by up to 18.6 points.

Researchers present CASHEWS, a JavaScript preprocessor for LLM-based malicious package detection that deobfuscates code iteratively, extracts bundled modules and dynamically executed code, identifies malicious sinks, and computes backward slices to produce compact detector input. Threat actors evade LLM detectors by exploiting limited context windows with high token-density obfuscation and by bundling malicious code with benign packages, as seen in supply-chain attacks such as Shai-Hulud. Across 512 large package files, two scanner types, and three LLMs, CASHEWS raised analysis coverage from 69.1-85.7% to 98.8-100% and reduced false-negative rates by up to 18.6 percentage points. Median preprocessing time is 30 seconds while net analysis cost drops 34.6%.

arXiv cs.CRSep 16, 16:00 UTC · 12h agoResearch

Echo: Learning-based Matching Decompilation using Trusted Back Translationnew

Researchers introduce Echo, a matching decompiler using trusted back-translation that roughly doubles exact-match rates and outperforms GPT-5.6 and Codex on Mirai.

Echo performs matching decompilation by using compilation as trusted feedback for iterative search: a domain-specific model generates candidate code and compilation configurations, which are recompiled, compared at assembly level, and repaired via rule-based rewriting, neural refinement, and reasoning-based refinement. On function-level benchmarks, Echo produces 2.43x more exact matches than the strongest baseline and the highest structural similarity to ground truth. On the Mirai malware binary, it matches 2.75x and 7.4x as many functions as GPT-5.6 and Codex, respectively.

arXiv cs.CRSep 16, 14:14 UTC · 14h agoResearch

12 Best Container Security Tools Compared (2026): Features & Pricing

GBHackers compares pricing and features of 12 container security platforms, from free Trivy, Falco, and SUSE NeuVector to commercial Sysdig, Wiz, and Aqua.

A procurement-focused comparison of twelve container security vendors including Sysdig, Wiz, Aqua Security, SUSE NeuVector, and CrowdStrike, centered on billable units (per node, workload, developer, or vCore) and pricing mechanics. The piece argues free open-source tools like Trivy, Falco, and NeuVector set a floor that commercial products must justify exceeding through enforcement and scale. It also notes rising container threats, including Kubernetes flaws exploited to jump from containers to cloud accounts and exposed container registries.

GBHackersSep 16, 09:46 UTC · 18h agoIndustry 14 sources1