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Hackers Advertise Uncensored Luciferus AI Service on Underground Forums
Sophos CTU found Luciferus, an uncensored criminal AI subscription service on the Exploit forum that returned RAT source code on request.
Sophos Counter Threat Unit discovered Luciferus advertised on August 24, 2026 on the Exploit forum by persona 'Optimus_Prime', claiming a proprietary 120-billion-parameter uncensored model that analysts assess with low confidence may be built on Alibaba's Qwen. Subscriptions run $35-$75 monthly, with a VIP 'Individual Embodiment' tier offering a separately deployed model trained on customer data. In testing, the Junior model generated Python remote-access-trojan source code, though Sophos did not execute or verify it. The service extends the WormGPT/FraudGPT lineage into structured commercialization with tiered pricing resembling mature SaaS businesses.
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Uncensored AI sold on hacking forum as alternative to ChatGPT and Claude jailbreaks
Sophos found Luciferus, an uncensored AI subscription service likely built on Qwen, sold on the Exploit forum and capable of generating working malware code.
Sophos Counter Threat Unit found an ad for 'Luciferus' posted August 24 on the Exploit forum by a persona named 'Optimus_Prime', claiming a proprietary 120-billion-parameter model that answers requests without ethical restrictions. Sophos assesses with low confidence it is based on Alibaba's open-source Qwen family. Forum tiers cost $35-$75/month, while the website lists Junior/Middle/Pro tiers at $22-$47.14; a test prompt on the Junior tier returned Python remote access trojan source code. Sophos warns such services lower barriers for less skilled cybercriminals and outlast jailbroken mainstream LLMs.
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How much of F-Droid is LLM generated?
A FOSS maintainer manually graded 102 F-Droid apps from the September 12, 2026 update batch, finding many show signs of LLM-generated code.
A student and FOSS app maintainer reviewed 102 apps pushed to F-Droid on September 12, 2026, assigning each a three-tier rating for likelihood of LLM-authored code (mostly AI >50%, hard to say/mostly human, no signs of AI). The heuristic relies on commit aesthetics, README and branding style, and the presence of agentic infrastructure like Claude Code or Codex, which automatically places an app in the 'mostly AI' tier. Example ratings include Amber (Nostr event signer) as mostly AI, and Aria for Misskey as showing no AI signs. The author stresses reliable detection of LLM-generated code from text alone is impossible, so ratings are approximate.
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Coding Is Over. Get over It
A JPMorgan Chase engineer reflects on AI agents outpacing hand-coding, questioning ROI while predicting inference costs will become negligible.
A personal essay by a software engineer with 15+ years at JPMorgan Chase describes how AI coding agents now outperform him and have transformed his workflow. He argues AI ROI is unmeasurable, praises cheap small models such as GPT 5.6 Luna, and cites Claude Code head Boris Cherny's advice to discard AGENTS.md/Claude.md rule files. He contends coding is no longer scarce, worries entry-level jobs will be automated first, and predicts AI's bigger impact lies outside coding.
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[AINews] DeepSeek v4.1-Flash: 763B-P8B-D16B novel causal Encoder–Decoder architecture with vision marks the Return of the Whale
DeepSeek released V4.1-Flash, an open-weight 763B-parameter model with a novel causal encoder-decoder architecture, 1M context, vision input, and MIT license.
DeepSeek launched V4.1-Flash, an open-weight MIT-licensed model using a novel causal encoder-decoder architecture with 763B total parameters and asymmetric active parameters: 8B for prefill and 16B for decode. It supports 1M-token context and text+image input, priced at $0.30 per 1M input and $1.20 per 1M output tokens with a 50% off-peak discount. Artificial Analysis scored it 40 on its Intelligence Index, above DeepSeek V4 Pro 0813, and Vals ranked it the #1 open-weight model ahead of Kimi K3. Baseten shipped day-0 support and Ollama began rolling it out to paid subscribers.
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Ask HN: Anyone still coding like 2021? Where do you work?
Hacker News users debate coding without LLMs, with one developer fired for refusing AI tools and others describing daily hand-coding practice to counter skill atrophy.
An Ask HN thread collects experiences of developers who still write code without LLM assistance. One contributor says he was fired for political reasons after refusing to use LLMs despite adequate stated performance, and observes fewer job ads now require LLM use. Others describe starting each day with a LeetCode problem or 30-60 minutes of hand-coding to stay sharp, contractual bans on AI-generated code for a government-adjacent embedded product over unresolved copyright issues, and inconsistent corporate policies where ChatGPT or Codex use flip-flops between allowed and blocked while a CIO mandates 70-80% AI-generated code next year.
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Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails
Research shows on-policy expert correction, not imitation fine-tuning, lets weaker agent models catch up under evolved harnesses.
Researchers study how to combine automated agent-harness evolution with lightweight fine-tuning across seven enterprise agent tasks. Naively training weaker models (Qwen3-Coder, Gemma 4) on expert trajectories under an evolved harness regressed performance by 4 to 30 points on all tasks. They propose an on-policy correction pipeline, automated by a meta-level MLE agent, where an expert rewrites only the failing turn of the weaker model's rollout, preserving model-harness fit.
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Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails
Research shows imitation of expert trajectories breaks weaker models' harness fit, while on-policy expert correction preserves gains across seven enterprise agent tasks.
The paper studies combining automated agent-harness evolution with lightweight fine-tuning across seven enterprise agent tasks using Qwen3-Coder and Gemma 4. Training weaker models on complete expert trajectories under an evolved harness regressed performance by 4-30 points on all tasks, disrupting model-harness fit. The authors propose an on-policy expert-correction pipeline, automated by a meta-level MLE agent, that rewrites only failing turns and preserves the model's planning style.
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Release Notes: Faster TI Investigations, Fresh Threat Research, and 650+ Threat Coverage Updates
ANY.RUN August release adds TI Lookup connections view, 81 behavior signatures, 16 YARA rules, 559 Suricata rules, and three new threat intelligence reports.
ANY.RUN released August product updates expanding its Threat Intelligence Lookup with a Connections block for pivoting between related observables (domains, IPs, URLs), JSON export for retrohunting and SIEM/NDR integration, and hidden whitelisted data by default. Detection coverage grew with 81 new behavior signatures, 16 YARA rules, and 559 Suricata rules covering malware execution, phishing, and C2 traffic. Three new Threat Intelligence Reports cover a US-focused RMM phishing campaign across 46 countries, the Mirage2FA phishing-as-a-service targeting Microsoft 365 (1,249 sandbox sessions, 9,332 potential compromise events), and a threat brief on OVERLORD RAT, CRPX0, and TRIBACK loader.
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DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF — new model trending #8 on Hugging Face
A new Qwen3.8-27B GGUF fine-tune claims ARC-C 735 at 8-bit with thinking tokens cut 2x-10x versus the base model.
Independent creator DavidAU released a GGUF fine-tune of Qwen3.8-27B built with Unsloth, claiming ARC-C of 735 at 8-bit and 719 at 4-bit, trending #8 on Hugging Face. The 'TURBO' variant cuts thinking tokens by one half to as much as one tenth while retaining output quality and detail. The repo ships both regular and MTP quants and claims gains over the base model across seven benchmarks, using 'Cold Fusion (GAIN + Unsloth)' and 'Fable Fusion 711' training methods.
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Risky Bulletin: Slovakia finds Russian backdoor in traffic speed cameras
Slovakia's NBU found an SMS-triggered backdoor in Russian-made NERO R-ONE traffic cameras, pausing a 279-unit deployment.
Slovakia's national security service NBU issued an alert against NERO R-ONE high-speed traffic cameras after finding a backdoor that grants shell and network access via SMS from hardcoded Russian phone numbers. The cameras are a rebranded version of the Russian CORDON PRO.M model by St. Petersburg firm Semicon, purchased via a Cyprus shell company under a €30 million EU-funded project. The report also found SecureBoot disabled, vulnerable web management, and unauthenticated live streams; the Interior Ministry paused deployment of 279 cameras pending independent assessment.
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