How to poison AI

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Good morning. It’s Monday, July 7th.

On this day in tech history: In 1998, NASA’s Deep Space 1 mission was in the final stages of preparation for its launch (occurring later in October). This phase involved integrating an AI-based autonomous navigation system, AutoNav, one of the earliest uses of AI in space exploration for real-time decision-making.

In today’s email:

  • How to poison AI: Reasoning Gets Derailed With Random Facts

  • Trea Agent for coding

  • Telerobotic Chefs Cook With No Delay

  • 5 New AI Tools

  • Latest AI Research Papers

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Today’s trending AI news stories

How to Poison AI: "Cat attack" on reasoning model shows how important context engineering is

A new study shows advanced language models can be derailed by trivial context. Researchers used a system called CatAttack to inject harmless phrases like “cats sleep most of their lives” into reasoning prompts, tripling error rates in models like DeepSeek R1. The attack works by having one model generate distractors, another judge their effectiveness, then testing them on top-tier LLMs.

Simple prompts—like cat facts or broad financial tips—can destabilize model reasoning, revealing just how brittle AI systems remain. | Image: Rajeev et al.

Even vague financial advice or suggestive guesses (“Maybe it’s around 175?”) caused failures and bloated token counts - what researchers call “slowdown attacks.”

Suffix attacks can spike DeepSeek-R1’s error rate by up to 10x, with the sharpest failures seen on math benchmarks. | Image: Rajeev et al.

In math benchmarks, error rates spiked up to 10×. The core issue: models still can’t separate signal from noise. Experts warn that in high-stakes fields like finance or healthcare, irrelevant context can trigger real-world failures. Read more.

‘Trae Agent’ Thinks, Fixes, and Ships Code

ByteDance just dropped Trae Agent, a command-line, LLM-powered software engineer that writes, debugs, and fixes code on demand. It handles full-stack dev tasks from natural language prompts and runs workflows autonomously using shell access, structured reasoning, and real-time patching. Backed by top-tier models like Claude and Gemini, Trae parses huge codebases, generates precise bug fixes, and reports results live through a built-in summarizer.

It crushed SWE-bench Verified with a streamlined toolset: file editing, bash execution, code graph lookup, and a sequential thought engine. The system is modular, model-agnostic, and open-source under MIT. ByteDance is betting on dev agents that don’t just autocomplete.

Trae Agent is now live on GitHub. Read more

Telerobotic Chef Cooks Steak from 1,800km Away

A DOBOT robotic arm, remotely operated from Shenzhen, flawlessly grilled steaks 1,800km away in Shandong. No lag, no errors, just real-time, high-fidelity control. It’s a sharp demo of mature remote presence tech, where latency, precision, and stability align.

Beyond kitchen theatrics, the impact is real: remote caregiving, disaster response, high-risk industrial ops - this is telerobotics stepping up and showing it’s ready for the field. Read more.

5 new AI-powered tools from around the web

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