Dan Goodin writes that ASCII smuggling, a technique once primarily used to hide malicious prompt injections from Large Language Models (LLMs), has been adopted by spammers to evade email filters. By using invisible Unicode tags that mimic the structure of standard text, attackers can bypass machine learning-based spam detectors and natural language processing models without alerting human readers. This method allows words like "funding" or "credit" to be broken into non-standard tokens that escape keyword detection while remaining perfectly readable to a person once rendered in an email client.
- ASCII smuggling uses the Unicode Tags block, which contains 128 characters designed to be invisible to humans but readable by computers.
- Microsoft observed spam signatures using this technique spike from 21,000 per day to over 2.5 million within a four-day period in early February.
- The method is effective against modern AI-driven filters because it disrupts the way tokenizers process words into sub-word pieces.
- While originally used for stealthy prompt injections, spammers now use it specifically to obfuscate financial keywords from automated detectors.
- ASCII smuggling uses the Unicode Tags block, which contains 128 characters designed to be invisible to humans but readable by computers.
- Microsoft observed spam signatures using this technique spike from 21,000 per day to over 2.5 million within a four-day period in early February.
- The method is effective against modern AI-driven filters because it disrupts the way tokenizers process words into sub-word pieces.
- While originally used for stealthy prompt injections, spammers now use it specifically to obfuscate financial keywords from automated detectors.
>"Using DSPy to automatically create, evaluate, and optimize your prompts"
Manual prompt engineering is often slow and unreliable due to unpredictable inputs. DSPy addresses this by treating prompt development like traditional ML training. It automatically generates, evaluates (using "LLM-as-a-judge"), and optimizes prompts based on high-level task descriptions, providing a faster, more systematic way to build robust LLM applications.
Manual prompt engineering is often slow and unreliable due to unpredictable inputs. DSPy addresses this by treating prompt development like traditional ML training. It automatically generates, evaluates (using "LLM-as-a-judge"), and optimizes prompts based on high-level task descriptions, providing a faster, more systematic way to build robust LLM applications.
This document defines the "cid:" and "mid:" URL schemes for referencing messages and their parts. The "cid:" scheme is used within multipart messages, like emails, to reference embedded content such as images. The "mid:" scheme allows referencing entire messages or specific parts within them using their Message-ID and Content-ID. It details the syntax for these URLs, emphasizing the importance of unique Content-IDs and the handling of encoded characters. The specification requires implementations to support the full "mid" URL form (message-id/content-id) for interoperability.
This article details how to set up an email triage system using Home Assistant and a local Large Language Model (LLM) to summarize and categorize incoming emails, reducing inbox clutter and improving email management. It covers the setup of a REST command to interface with Ollama, the automation process, and the benefits of using a local LLM for privacy.
A Model Context Protocol (MCP) server that provides tools for interacting with JMAP (JSON Meta Application Protocol) email servers. Built with Deno and using the jmap-jam client library.
DispatchMail is an open source locally run (though currently using OpenAI for queries) AI-powered email assistant that helps you manage your inbox. It monitors your email, processes it with an AI agent based on your prompts, and provides a (locally run) web interface for managing drafts/responses, and instructions.
The article discusses how AI agents can help business owners manage their inboxes by sorting emails, filtering spam, and automating responses. It highlights the benefits of AI in maintaining productivity through deep work, updating business processes, and enabling personalized communication. The author suggests implementing safeguards to ensure accuracy and appropriateness in email communications.
In Thunderbird email client, tags are used to organize and group messages manually or through filters. They can be assigned and removed via the context menu, toolbar, or message header, and are case-insensitive. Tags are stored differently depending on whether you use POP or IMAP accounts, and their color is determined by the lowest-numbered tag assigned. The article also discusses differences between tags and labels, interoperability with other email clients, and various add-ons that enhance tag functionality.
