Cybercrime is growing at a dizzying pace and projected to inflict $9.5 trillion USD global cost in 2024. Java-based applications, often targeted by hackers due to their known and zero-day vulnerabilities, are at high risk. The increased reliance of many Java applications on open source frameworks such as Spring
Large Language Models (LLMs) have evolved from being merely passive text generators with limited capabilities to becoming autonomous or semi-autonomous agents navigating complex environments and offering actionable insights. This transformation equips them with a diverse set of tools, perception modules to interpret signals from various modalities, and memory systems to
Multimodal Large Language Models (MLLMs) are garnering significant attention. There has been a plethora of work in recent months dedicated to the development of MLLMs [Flamingo, NExT-GPT, Gemini...]. The key challenge for MLLMs lies in effectively injecting multimodal-data in LLMs. Most research begins with pre-trained LLMs and employs modality-specific encoders
Large Language Models (LLMs) are developed to understand the probability distribution that governs the world language space. Autoregressive models approximate this distribution by predicting subsequent words based on previous context, forming a Markov chain. World knowledge (often referred as parametric knowledge) is stored implicitly within the model's parameters.
Pre-reading Requirements In this post, I assume you have a basic background in software and cybersecurity engineering. However, even if you're not highly technical, don't worry, I will ensure that you can grasp and understand the intricacies of the vulnerability and the exploit, as well as
Large Language Models (LLMs), exemplified by OpenAI’s GPT-4 and Meta’s LLaMA, continue to impress us with their capabilities, which have surpassed expectations from just a few years ago. Recently, the research community has shifted its focus towards the optimal and efficient usage of resources. Concepts like the Mixture
DAST and black-box approaches are methods used to test the security of an application by analyzing its behavior in response to inputs without having knowledge of the application’s internal structure or the code being executed. These approaches rely on generating inputs through methods such as brute force and randomness,
Traditional security measures, including firewalls, intrusion detection systems and AVs aim to prevent malicious activities by identifying and blocking known threats before they can cause harm. These security measures frequently employ signature-based detection methods, complemented by heuristic, machine learning and behavior analysis techniques. RASP (in short for Runtime Application Self
Transformer-based deep learning models, such as GPT-3 and LLaMA, have achieved state-of-the-art results on many NLP tasks. These models have exhibited outstanding performance and are capable of resolving tasks on the fly through in-context-learning (ICL) without the need for retraining. This approach helps to avoid the well-known catastrophic forgetting problem.