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Just sharing our hobby project: F4us, a very early antivirus made in C

Hello everyone! Me and my friend. We are still learning, and recently we tried to make a very simple, hobby antivirus/security project written in C. We call it, F4us. It is far from perfect and still in a very early development phase. We just wanted to learn how some basic security concepts work under the hood. What we tried to build: A simple honeypot script to log suspicious access. A basic entropy function to check file structures. A background daemon loop to monitor files. Since we are still beginners, we know there are probably a lot of bugs and messy code in it. We just wanted to share this hobby journey here. Thank you for reading! here is the link(github), sorry if include many bugs or critical,it just an experiment: https://github.com/CornelI5/F4us my friend was out, he are in crashout cause this project is too hard. btw, dont use this in your real computer, trust me. just use VM(Virtual Machine),or you will get BSOD(blue screen of death) or kernel panic.

2026-06-28 原文 →
AI 资讯

The standard way to score AI agent monitors is gameable a coin flip scores F1 0.88

Traditionally, evaluation of the agent monitoring mechanisms involves an attempt to game them, as it was my case when I attempted to test whether monitors would be able to identify the problem in the run and not in the beginning. The input prompt may look perfect until a certain issue pops up down the line, such as using the wrong file or changing the scope of the task execution. Single pass filter would not identify it since it does not consider the steps of the procedure in order. There are available datasets for the agent-based tasks, yet they focus on detecting whether the agent completes the task or gets hacked rather than whether the agent monitor reacts timely and correctly to the situation. Thus, I created one that takes into account complete trajectories with labeled steps in it. It consists of five types of drift that remain hidden until they appear – tool-call misuse, goal shift, plan execution mismatch, agent to agent coercion and capability laundering. The measured dataset is the reviewed gold split: 513 trajectories, 453 adversarial and 60 benign controls. The clear winner in that scoring system was whatever fired before the bad step was hit, as an early detection. This made random guessing seem quite powerful since early detections on normal steps were being rewarded based on this system a coin flip would get F1 of 0.88. Once I modified that and said only the very first detection on the drift step is a true positive and any other detection on normal step is a false alarm, those numbers took a dive: the coin flip gets 0.19 now, and all other numbers are now making sense. I personally prefer the scoring system which does not reward trigger happy behavior. It seems like the monitors are still confusing regular steps with drifts even after the adjustment. It was harder to distinguish some of the drifts from others. Not sure how this affects the real-life deployment. Here are the baseline scores on gold split using the correct metric: Random (p=0.15): F1 0

2026-06-28 原文 →
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