Slips
Stratosphere IPS
How Well Do LLMs Perform on a Raspberry Pi 5?

Can a Raspberry Pi 5 run Large Language Models? In this post, we share the results of our experiments, analyzing how LLMs perform on this low-cost hardware and exploring the challenges and performance trade-offs.

How Well Do LLMs Perform on a Raspberry Pi 5?
Stratosphere IPS
Introducing ARACNE, a new LLM-based shell ...

The complete automation of cyber-attacks has become one of the areas of greatest interest since the introduction of Large Language Models (LLMs) to the public. The creation of attacking LLM agents that can act independently is among the most popular options. 

In this blog, we introduce a brand-new agent: ARACNE. We also share the results of attack tests and what they mean in terms of the agent’s current capabilities.

Introducing ARACNE, a new LLM-based shell pentesting agent
Teaching
Stratosphere IPS
Czech Technical University in Prague’s ...

We are thrilled to announce that, for the first time, the Czech Technical University in Prague is offering the "Introduction to Security" course as a Massive Open Online Course!

Czech Technical University in Prague’s "Introduction to Security" Class is now a Free Online Course!
Stratosphere IPS
Google Summer Of Code 2024 In Stratosphere. ...

This blog covers my ongoing GSoC project 2024. It provides information about the current progress of my work, as well as my experiences and lessons learned along the way.

Google Summer Of Code 2024 In Stratosphere. Blog for the first month of Sekhar Kumar Dash
Slips, GSoC2023
Stratosphere IPS
Announcing Google Summer of Code 2023 Projects

Stratosphere is happy to announce the accepted projects in the Google Summer of Code (GSoC) 2023!

Announcing Google Summer of Code 2023 Projects
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Guest Post: A Graph-Based Approach to Cyber Threat Intelligence

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Guest Post: A Graph-Based Approach to Cyber Threat Intelligence

A university project turned into a powerful tool: Rocío Baggio and Diego Forni’s graph-based system connects malicious IPs, attack techniques, and threat actors—giving cybersecurity analysts clearer insights into the ever-evolving threat landscape.

Exploring LLMs for Cybersecurity: Our ICAART 2024 Extension Paper

Exploring LLMs for Cybersecurity: Our ICAART 2024 Extension Paper

We’re excited to share our new ICAART extension paper, published in the Lecture Notes in Artificial Intelligence series. The paper explores how Large Language Models (LLMs) can be leveraged as agents for network security testing, outperforming traditional reinforcement learning methods in several scenarios. This research, including the introduction of our new NetSecGame environment, demonstrates the promise of LLMs in cybersecurity applications.

Introducing ARACNE, a new LLM-based shell pentesting agent

Introducing ARACNE, a new LLM-based shell pentesting agent

The complete automation of cyber-attacks has become one of the areas of greatest interest since the introduction of Large Language Models (LLMs) to the public. The creation of attacking LLM agents that can act independently is among the most popular options. 

In this blog, we introduce a brand-new agent: ARACNE. We also share the results of attack tests and what they mean in terms of the agent’s current capabilities.

Czech Technical University in Prague’s "Introduction to Security" Class is now a Free Online Course!

Czech Technical University in Prague’s "Introduction to Security" Class is now a Free Online Course!

We are thrilled to announce that, for the first time, the Czech Technical University in Prague is offering the "Introduction to Security" course as a Massive Open Online Course!

Towards Better Understanding of Cybercrime: The Role of Fine-Tuned LLMs in Translation

Towards Better Understanding of Cybercrime: The Role of Fine-Tuned LLMs in Translation

Our paper explores the use of Large Language Models as mechanisms to translate public hacktivists messages from Russian to English as a way to address all these problems. We show how our method can achieve high-fidelity translations and significantly reduce costs by a factor ranging from 430 to 23,000 compared to a human translator.

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