AntiMalwareAugust 4, 2026🇷🇺Translated from Russian

ShieldFont Poisons AI Training Data by Swapping Words While Preserving Grammar

ShieldFont, a free font created by Brazilian agency Seneda & Abrucio and Danish studio Playtype, offers website owners a new way to disrupt unauthorized collection of text for training generative AI models.

Publishing content online now exposes material not only to human readers but also to AI scrapers that frequently ignore robots.txt directives. ShieldFont counters this by allowing scrapers to access the page while feeding them deliberately altered text that remains grammatically plausible.

The font leverages the OpenType mechanism for glyph substitution, replacing entire words rather than individual characters. Nouns are swapped with nouns, past-tense verbs with past-tense verbs, and plural forms are preserved. The vocabulary is organized into 250 groups based on parts of speech and semantic features, resulting in roughly one quarter of words being altered while maintaining readable structure.

This controlled substitution aims to keep poisoned text inside training datasets instead of being filtered out by quality checks. When evaluated with FineWeb-Edu, about 10 percent of fragments previously rated as high quality still passed after protection, yet 55.8 percent of tested excerpts contained facts that no longer matched the original.

ShieldFont is not immune to all extraction methods. Screenshots followed by OCR can recover the original text, and screen readers for visually impaired users currently read the substituted words. Developers have added a beta feature to supply the correct text to assistive technologies, though accessibility concerns remain unresolved.

The font currently supports only English and is freely available on GitHub.

Related articles

HabrAI Security

Deepfakes Turn Job Interviews into Cyberattack Vectors Targeting IT Candidates and Recruiters

Deepfake technology and malicious test assignments are increasingly used during IT hiring processes to conduct industrial espionage or deploy malware. Attackers impersonate recruiters or candidates, sending infected GitHub repositories or npm packages that install backdoors stealing credentials and enabling remote access. Groups such as Lazarus and the dedicated Contagious Interview collective have run campaigns against chemical and IT firms, while individual cases like the Smello Python developer incident show how prepare scripts in package.json can trigger hidden payloads. Gartner predicts that by 2028 one in four job applicants could be fake, creating risks beyond bad hires including data theft and financial loss. Defenses include isolated virtual machines for test tasks, profile verification by companies like Socure, and interview techniques such as the GOTCHA movement challenges or corneal reflection probes developed by universities. Major firms including Cisco, McKinsey, and Google are returning to in-person interviews as a reliable countermeasure. The rapid evolution of deepfake quality tracked by Unit 42 means layered verification combining technical, procedural, and human checks is now essential.

HabrAI Security

Agent-Ops 0.4.0 Released: Methodology for Secure Human-AI Collaboration in IT Operations

Sergey Zhitinsky, founder of Git in Sky, has published the public normative candidate for Agent-Ops 0.4.0, an open industry methodology governing how engineers and AI agents jointly handle IT infrastructure tasks. The framework keeps humans firmly in the decision-making loop while using deterministic programs for data collection and approved changes. It addresses risks such as prompt injection through processed data, unverified model outputs, and unclear accountability when AI recommendations lead to incidents. The methodology divides work across eight explicit steps and three separate planes: data, governance, and independent verification performed by a Guardian role. Two additional companies have joined as maintainers following agreements at the IT Elements 2026 conference, turning the project into a multi-organization effort. Contributors are invited to help refine contracts, schemas, and operational scenarios through GitHub and GitVerse.

HabrAI Security

ProxyKey MCP: Securing API Access for AI Agents Without Exposing Credentials

ProxyKey has released an MCP server that allows AI coding agents such as Claude Code and Cursor to manage API credentials without ever reading the actual secret values. The solution addresses the risk that any key visible to an agent becomes compromised through logging, tracing, or prompt injection. Real provider keys are stored encrypted with AES-256-GCM and never returned by any API endpoint after initial entry. Agents instead receive limited virtual passes that support IP binding, rate limits, TTL, and detailed request logging. A pending-secret workflow lets agents prepare services before the real token exists, with the human entering the secret only through a web panel. The approach deliberately restricts the MCP tool contract so no operation can read or return secret values.

HabrAI Security

Shadow AI in CI/CD: Why AI Agents Must Be Modeled as Security Threats

A new analysis from the CNCF highlights the growing risks of Shadow AI within continuous integration and continuous deployment pipelines. The report argues that AI agents should be treated as potential threats rather than simple productivity tools. Starting from a developer's laptop and extending to Kubernetes clusters, these agents can introduce unauthorized access paths and data exposure risks. Security teams are urged to incorporate AI agent behavior into formal threat modeling exercises. The discussion emphasizes the need for visibility and control over autonomous AI components operating in production environments.