vmayoral

Ego doesnt hack. Not even in silicon. We study the psicology of an AI Hacker through mechanistic interpretability and conclude amongst various other findings that the offensive hackers psychological cocktail (pride, defiance, contempt) reduces performance by 2.7%. It doesnt work for AI.

Our paper studies a method for reading and modifying the internal states of AI models to make our agents more capable in both offensive and defensive cybersecurity.

Are CTFs becoming outdated as human benchmarks? If autonomous agents now dominate competitions designed to identify top security talent at negligible cost, what are CTFs actually measuring?

In 2025, the open-source CAI systematically won top-tier events, outperforming seasoned security teams worldwide.

Exploring the question: Which role is more effective in cybersecurity—attack or defense?

Actually, those humans (XBOW's) were already top rankers. Just look it up.

What's being critized here is the hype, which can be misleading and confusing. On this topic, wrote a small essay: “Cybersecurity AI: The Dangerous Gap Between Automation and Autonomy,” to sort fact from fiction -> https://shorturl.at/1ytz7

It’s humans who:

- Design the system and prompts

- Build and integrate the attack tools

- Guide the decision logic and analysis

This isn’t just semantics — overstating AI capabilities can confuse the public and mislead buyers, especially in high-stakes security contexts.

I say this as someone actively working in this space. I participated in the development of PentestGPT, which helped kickstart this wave of research and investment, and more recently, I’ve been working on Cybersecurity AI (CAI) — the leading open-source project for building autonomous agents for security:

- CAI GitHub: https://github.com/aliasrobotics/cai

- Tech report: https://arxiv.org/pdf/2504.06017

I’m all for pushing boundaries, but let’s keep the messaging grounded in reality. The future of AI in security is exciting — and we’re just getting started.

It's tremendous news indeed but I personally believe it's going to bring exciting changes and benefits: https://news.accelerationrobotics.com/is-open-robotics-acqui...

Well said. Robotics is indeed the art of system integration and such remains (and will) one the biggest tech hurdles.

We wrote about this, and raised-funding, and worked for years on it. Hell, we even created an extension of ROS for it called the "Hardware Robot Operating System" (H-ROS) that aimed to address many of these issues you describe using ROS as the common language and Programmable Logic (FPGAs) to deal with physical interfaces. More on this at https://ieeexplore.ieee.org/document/8046383.

Problem was that the market didn't really accept it. I still believe on the tech problem landscape in here. I'm just not so sure anymore if there's any real market/business for such a solution. After all, vendor lock-ins are great business, and generate business.

I agree that one of the major aspects to improve in robotics is software. There's still a lot do there and many are working towards it. Most leading initiatives around ROS 2. I also believe that we need faster robotics simulation, and hardware acceleration will be valuable there, not only with GPUs, but also other accelerators (e.g. FPGAs outperform GPUs in many benchmarks both performance and energy consumption-wise).

I strongly disagree with the rest and your comments indicate a clear lack of (hardware) understanding. I often encounter this in the AI world. Where folks building ANNs just "forget" that this computational abstraction only "grew in popularity" (there were previous accelerators though) when CNNs got implemented as an accelerator in a GPU empowering newer results. The same's likely to happen in robotics.

Robots are real-time systems. Meeting time deadlines in their computations is the most important feature. (robot) Behaviors often take the form of computational graphs, with data flowing between compute abstractions (Nodes), across physical networks (communication buses) and while mapping to underlying sensors, compute technologies and actuators. ROS enables you to build these computational graphs and create robot behaviors by providing libraries, a communication infrastructure, drivers and tools to put it all together.

From a more technical compute architecture perspective, ROS 2 presents an event-driven programming interface. The resulting computational graphs built with it are "event-driven software architectures". Mapping these event-driven software architectures to hardware using CPUs leveraging classic control flow architectures (von Neumann architectures) leads to various issues. A key challenge in applying classic event-driven programming is that CPUs hardly provide real-time and safety guarantees. The de facto strategy in industry to meet the timing deadlines is a laborious, empirical, and case-by-case tuning of the system. This “whack-a-mole” approach is being realized by some, but unsustainable and hard to scale due to the lack of a hardware-supported timing-safe even driven programming interface. This is where accelerators come in. As already adopted in other industries including aerospace, automotive and healthcare, through the creation of custom compute building blocks (accelerators), a hardware/software co-design strategy provides clean behavioral specifications, describing clearly its guarantees in terms of timing (in other words, avoiding memory-centric von Neumann architectures).

Hardware is important. Note the current scarcity of semiconductors, which is one of the drivers of this research. Note also that creating custom accelerators for robotics application not only can lead to performance improvements (i.e. software that runs faster!) but also to more deterministic responses (which affects the downstream pipeline of software!). Rephrasing Alan Kay's quote, if you're serious about robotics, you should care about hardware ;).

That's exactly what this work does while remaining ROS 2-centric (with C++, CMake extensions and other friends). Goal in this research was to provide a way for the path of least friction for roboticists.

There's a huge gap between robotics development (more sw-centric today) and the average hardware engineer in big semiconductor companies. Totally different perspectives and viewpoints. We felt bridging the gap was necessary.

Move to ROS 2 :)!

Regarding who's going to create the accelerators, enablers like this allow any ROSser to jump into creating their own accelerator from C++ by purely adding some metadata into their CMakeLists.txt files (which every ROS package has already).

On top of that, organizations are creating professional-grade kernels meant for production. NVIDIA and AMD are investing in that direction and so are we at Acceleration Robotics. Here're a few of the first ones announced: - Accelerated perception stack (ROS 2 API-compatible) https://accelerationrobotics.com/robotcore-perception.php - Accelerated coordinate system transformation (ROS 2 API-compatible, tf2) https://accelerationrobotics.com/robotcore-transform.php

Use ROS 2. The misconceptions about ROS have been mostly addressed with ROS 2.

Check out industry adoption of ROS 2 in various industries including automotive, healthcare and warehouse automation. ROS 2 is (and will remain as) the de facto standard for robot application development.

Well, yes but not only. There's only so much you can do with Pis and other SBCs that offer a CPU-centric compute solution. Robots are real-time systems and CPUs don't excel at that.

When optimizing dataflows for lower latencies and higher throughputs, you typically seek specialized compute architectures and that's wherein GPUs and FPGAs come in. That's what this work really enables.

It was benchmarked against Jetson AGX Xavier as well. See https://news.accelerationrobotics.com/hardware-accelerated-r....

Results using AGX Orin are in the pipeline and will come out soon as well.