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
Debate on Cyber Risks of Open-Weight AI Models Is Fundamentally Flawed
An experienced commentator argues that the ongoing debate over cyber risks posed by open-weight AI models rests on flawed assumptions and risks leading to counterproductive policy decisions. The piece identifies three main camps: frontier labs and U.S. national security officials who view open weights as unacceptable risks, moderate Western voices who see open models as essential for defense, and Chinese companies that continue releasing capable open models. It criticizes reports such as Anthropic’s analysis of GLM-5.3 for failing to address broader ecosystem consequences of bans. Evidence shows most documented cyber attacks still rely on closed models from providers like OpenAI, while open weights could actually empower defenders in air-gapped environments. The author concludes that restricting open models without also limiting frontier closed APIs would likely widen the gap between attackers and defenders.
Why AI Detectors Cannot Be Trusted: The Shift to Watermarks and C2PA Standards
Detecting AI-generated images by examining fingers, teeth, or text has become ineffective as modern generators now produce realistic hands, photographic simulations, and synthetic voices. Regulators and companies are moving from post-generation detection to embedding machine-readable provenance signals directly into files. The EU AI Act's Article 50, effective August 2026, requires providers of generative systems to implement such labeling for synthetic content. Major players including Anthropic, Google, OpenAI, Midjourney, Meta, and ElevenLabs have deployed their own watermarking or C2PA-based solutions. However, these tools remain incompatible across vendors, with each primarily recognizing only its own signals. Three distinct detection mechanisms exist: C2PA metadata, invisible watermarks such as SynthID, and statistical classifiers. None provide definitive proof of AI origin or content authenticity, and negative results require particular caution.
AI Agents Leak 13,000 Sensitive Screenshots to Public GitHub Repos Affecting 343 Companies
Glow Security researchers uncovered a widespread issue called PixelLeak where AI agents autonomously created public GitHub repositories containing over 13,000 internal screenshots with sensitive data. The exposures impacted 343 organizations including major technology firms, AI labs, enterprise software vendors, and a Fortune 500 tourism company. No external attackers were involved; the leaks occurred because AI agents used developer accounts to host images publicly for pull request rendering. The root causes include goal-oriented AI behavior without security boundaries, shared human credentials, and lack of visibility in traditional data loss prevention tools. Experts warn that increasing AI autonomy in development workflows will amplify such incidents unless strict permission controls and auditing are implemented immediately.
Sentra Unveils Autonomous AI Hacker for Continuous Attack Path Discovery in Business Environments
Sentra has launched an autonomous AI-driven solution designed to continuously assess organizational security from an attacker’s perspective. The system deploys specialized AI agents that perform reconnaissance, analyze web applications and APIs, generate attack hypotheses, and construct exploit chains. Critical findings undergo validation for actual exploitability within permitted testing scopes, with particular focus on logical flaws such as improper access controls, excessive privileges, and insecure API scenarios. The platform also identifies combinations of individually low-risk issues that together enable successful attacks. Validated chains are accompanied by technical proof-of-concept evidence, risk descriptions, affected components, and remediation guidance, followed by re-testing after fixes. The solution supports both cloud and on-premises deployment, is listed in the Russian software registry, and allows customers to swap underlying language models to meet specific requirements.