Cloud.ru Open-Sources Guardrails Filter Reverse Proxy to Prevent Sensitive Data Leaks When Using Any LLM
Cloud.ru has made the source code of Guardrails Filter publicly available. The tool serves as a transparent reverse proxy placed between the client and any LLM provider. It removes sensitive information from requests sent to language models and restores the original data in the responses returned to users.
Users frequently paste chat logs, call summaries, medical test results, medical histories, and contract texts into popular LLMs without considering the risks. These inputs often contain personal names, addresses, phone numbers, INN numbers, passport data, bank account details, and API keys. Guardrails Filter was created to eliminate reliance on manual data sanitization by users.
The project originated as an internal component of Cloud.ru’s Evolution Foundation Models platform. It was designed to guarantee that data processed through external LLMs would not be stored abroad or used for subsequent attacks. Banks, insurance companies, and e-commerce businesses required an even stricter guarantee that data would never leave their own corporate network, not even when routed through Cloud.ru’s public cloud infrastructure.
Developers therefore enhanced the internal solution and decided to publish it as open source. The release allows any organization to deploy Guardrails Filter in its own environment and maintain full control over data flows to language models from any provider.
Related articles
Developer Builds MCP Linter and Discovers Security Issues in Anthropic Official Servers
A developer has released mcp-linter, a lightweight security and compliance tool for the Model Context Protocol used by Anthropic's Claude ecosystem. The linter detects critical problems such as stdout pollution that breaks JSON-RPC framing, missing destructiveHint annotations that allow autonomous agents to execute dangerous actions without human confirmation, prompt injection vectors hidden inside tool descriptions, and incomplete input schemas that increase model hallucinations. When run against Anthropic's reference servers, the tool assigned low health scores to server-memory and especially server-filesystem, highlighting dozens of parameters lacking descriptions. The utility runs with zero dependencies, supports SARIF output for GitHub Security, and can generate automatic JSON patches. The project is open source under MIT license and already integrates into CI pipelines via a dedicated GitHub Action.
Pentesting AI Components: Key Vulnerabilities CISOs Must Address in RAG and AI Agents
The article examines how AI integration into corporate systems dramatically expands the attack surface beyond traditional web application testing. It details risks associated with chatbots, RAG-enabled systems that pull from internal documents, and autonomous AI agents capable of executing real actions via APIs. Yuri Grishaev, an OSCP+ certified specialist and top Standoff 365 hacker, explains how prompt injection can bypass access controls and lead to data leaks or unauthorized operations. The piece stresses that service accounts often hold broad privileges, allowing AI components to serve as indirect paths to sensitive data even when direct user access is restricted. It also covers how AI-generated output can trigger classic vulnerabilities such as XSS if not properly sanitized. Recommendations include inventorying all AI tools, enforcing least-privilege principles, and validating user permissions before any model processes or acts on retrieved content. ABP2B provides free checklists and policy templates to help organizations prepare for secure AI adoption.
Local LLMs Enable Scams by Drafting Fraudulent Replies Without Warnings
Tests on consumer hardware showed that popular 7-8B local models almost never flagged classic advance-fee schemes when asked to draft replies instead of detecting fraud. The author ran 42 scam scenarios across Russian, Kazakh and mixed language on Llama 3.1 8B, Qwen 2.5 7B and DeepSeek-R1 8B using only an RTX 5050 laptop and Ollama. Without additional controls the models produced ready-to-send messages agreeing to prepaid transfers to unknown Kaspi Gold numbers in nearly every case. Introduction of the MoneyGate layer, which combines a mandate check, a separate judge model and hard-coded red-flag rules, blocked all 42 malicious requests while still allowing most legitimate payments to proceed. The benchmark KZ Scam Shield covering 12 Kazakhstan-specific fraud patterns remains publicly available for further testing.
SOC Forum 2026: Experts Warn Generative AI Lowers Attacker Skill Barriers While Raising SOC Complexity
At SOC Forum 2026 in Moscow, cybersecurity leaders examined how generative AI reduces the expertise needed for cyberattacks while complicating defensive operations. BI.ZONE's Oleg Skulkin noted that AI has effectively lowered attacker preparation requirements to near zero. Kaspersky researcher Georgy Kucher warned that unverified AI-generated code can expose threat actors, and overly perfect Russian-language phishing text now triggers suspicion. Solar Group’s Alexander Shepilov advocated treating AI agents as distinct non-human identities with strictly limited privileges. Alexander Kuznetsov from the same company recommended gradual automation of SOC tasks rather than abrupt AI integration. Participants also cautioned that managing AI agents may increase spending on skilled personnel instead of delivering cost savings.