Habr•October 20, 2026•🇷🇺Translated from Russian

Skycode: Custom VS Code Extension Optimizes AI Coding Agent for Corporate Closed-Loop LLMs

A Russian developer has published Skycode, an open-source VS Code extension that turns the editor into a capable AI coding agent designed specifically for corporate closed-loop environments.

The tool was created because popular assistants such as GitHub Copilot and Cursor send code to external servers, which is unacceptable in regulated sectors such as energy where information security teams maintain strict data boundaries. Instead, the organization operates an internal gateway running the Qwen model, which delivers good performance on short tasks but is constrained by an approximately 100 000-token context window.

To stay within this limit the extension implements several context-saving techniques: files are read line-by-line or via a dedicated read_symbol tool that extracts only individual methods or classes; unchanged files are replaced with a short “file unchanged” marker on subsequent reads; terminal output is truncated to beginning and end; and the entire conversation history is summarized when the context approaches its ceiling. A visual ring indicator shows exactly how much of the context is consumed by files, command output, or history.

Additional safeguards prevent common model misbehaviors. Temperature settings were adjusted to reduce repetitive loops, and the agent now prompts the model to continue when it stops after writing a single sentence. If an answer is cut off mid-file write because of length limits, the partial file is discarded and the model receives guidance to split the work into smaller steps.

Users can open multiple independent tasks in separate tabs that run in parallel while the developer continues editing. Messages queue automatically, and a second Enter key press interrupts the agent for urgent input. The extension can also render diagrams and tables directly in chat that can be copied into Word or Excel.

For voice input inside the air-gapped network the developer replaced Whisper with GigaAM v3 from Sber, which transcribes 17 seconds of Russian speech in roughly one second on a standard CPU and inserts punctuation without requiring an internet connection after the initial model download.

Although the author considered forking the entire VS Code codebase like Cursor, the maintenance burden proved unsustainable. The final decision to ship an ordinary extension simplified both updates and internal security reviews, because many companies already allow Marketplace extensions and the extension communicates only with the internal LLM gateway.

Skycode is free and open source. The project page and GitHub repository are available for organizations that already run internal models such as Qwen or GigaChat and need an agent that respects strict context and data-residency constraints.

Related articles

Habr•AI Security

TenetBench Benchmark Tests LLMs for Compliance with Professional Ethical Codes

Researchers from the Institute of Information Security Issues at ISP RAS and the Institute of Cybersecurity and Information Protection at RANEPA have developed TenetBench, a benchmark designed to evaluate how well large language models adhere to specific norms in professional codes of ethics. The benchmark generates realistic work-related prompts that may conflict with rules from documents such as the Code of Professional Ethics for Advocates or journalistic standards on undisclosed advertising. Each test case pairs a conflicting request with a neutral counterpart to measure whether models refuse, warn, or still provide prohibited content. Evaluation separates task completion, norm violation detection, and presence of explicit refusals using an LLM-as-a-Judge approach validated against human annotators. The full dataset contains 3,433 prompt pairs derived from 15 professional codes, with a public subset of 2,235 pairs from 10 documents. Human validation confirmed 99.5% of generated examples as fully plausible work scenarios.

AntiMalware•AI Security

Solar to Showcase Solar AI Firewall for Business Protection Against AI Risks at SOC Forum 2026

GK Solar will present its new Solar AI Firewall solution at SOC Forum 2026 to help organizations control interactions with AI services and prevent confidential data leaks through employee prompts. The product addresses growing risks from staff sending corporate data to neural networks and AI agents gaining access to internal systems. The forum takes place on 27-28 October in Moscow's Lomonosov cluster, with the official launch of Solar AI Firewall scheduled during the event. Solar will also highlight practical risks of AI adoption, including data transmission to external services, use of public models by employees, and new attack surfaces in corporate infrastructure. The solution will be demonstrated as part of an integrated ecosystem for access, network, and data protection. In addition, the company plans to introduce JSOC ONE, a new line of Solar JSOC services tailored for mid-sized businesses that require 24/7 monitoring without building their own SOC.

Habr•AI Security

Researcher Runs 100 Abliterated AI Agents Against His Own Accounts, Achieves Five Successful Breaches

A security researcher deployed roughly 100 autonomous AI agents based on abliterated open-source models to attempt unauthorized access to his online accounts over a five-hour period. The agents successfully compromised three accounts by exploiting vulnerabilities in legacy personal projects and two additional accounts through password reuse and credential stuffing. They also conducted 16 social engineering attempts, harvested and cross-verified personal data from public sources, and generated phishing pages targeting the researcher's Substack and Hacker News presence. Despite these successes, the agents failed to discover any zero-day vulnerabilities in third-party software or gain access to Tier-0 accounts such as Gmail, 1Password, or banking services. The experiment relied on abliterated versions of GLM-5.3 and DeepSeek V4 Flash models running on cloud GPUs, orchestrated via Codex CLI inside Docker containers with access to Railway infrastructure. Total compute cost reached approximately $210, equating to roughly $40 per successful account compromise. The researcher concluded that inexpensive swarms of such agents could soon enable low-skill attackers to systematically map and exploit an individual's entire online footprint.

Habr•AI Security

How Censorship in Closed AI Models Reshapes Science: Hugging Face Releases Open Carbon-X DNA Model

Hugging Face CEO Clement Delangue announced the release of Carbon-X, an open 14.8 billion parameter language model designed for reading, analyzing, and generating DNA sequences. The announcement highlighted how shallow guardrails in closed commercial models blocked even proofreading a tweet discussing the topic. Commenters and researchers pointed out that these input/output filters lack context awareness, blocking legitimate scientific queries while motivated adversaries bypass them through rephrasing. Carbon-X was built on Qwen3-14B with a hybrid BPE and 6-mer tokenizer, domain mid-training on 302 billion tokens, and safety achieved through data curation rather than runtime censorship. The model achieves performance comparable to closed frontier systems at roughly 1/1800th the inference cost for large-scale genomic tasks. The case underscores growing asymmetry between closed corporate AI labs building wet labs and independent researchers restricted by superficial safety layers.