Enhancing Firmware Security with ML & LLMs – From Component Discovery to Actionable Insights

Firmware analysis is inherently complex. Firmware images typically contain heterogeneous data, multiple processor architectures, compressed components, and little to no metadata. As a result, identifying software components, determining versions, and understanding the internal structure of firmware remains a challenging and resource-intensive task when relying on traditional heuristic approaches.

Machine Learning (ML) and Large Language Models (LLMs) introduce a fundamentally different approach. By learning complex, high-dimensional patterns from noisy and unstructured data, these technologies can generalize across diverse firmware formats, automate detection tasks, and uncover relationships that are difficult to identify with rule-based methods alone.

Because of these capabilities, ML and LLM approaches are increasingly becoming a powerful enabler for firmware analysis — improving accuracy, accelerating discovery, and surfacing insights at scale.

However, many organizations are still at an early stage in understanding how these methods work in practice, what kind of results they can deliver, and how they compare to traditional analysis techniques.

Without this understanding, companies risk missing opportunities to improve analysis quality, reduce manual effort, and scale their firmware security and transparency processes effectively.

In this 45-minute webinar, we present machine learning methodologies and empirical results for firmware analysis, focusing on architecture detection, component and version identification, and evaluation across real-world firmware corpora.

To provide a clear and practical perspective, we will first outline the key challenges of firmware analysis and the limitations of heuristic methods, then introduce ML and LLM-based approaches, and finally present empirical results demonstrating their effectiveness in real-world scenarios.

You will learn:

• Why firmware analysis is challenging and where traditional heuristic approaches reach their limits
• How machine learning models enable architecture detection across diverse firmware images
• How ML techniques support automated component and version identification
• How large language models can assist in reasoning, summarization, and interpretation of firmware data
• What empirical results from real firmware corpora reveal about the effectiveness and scalability of ML-based analysis

This webinar is designed for professionals working in product cybersecurity, as PSIRT Manager, or Head of Development who want to understand how advanced analysis techniques can improve visibility and efficiency.

Don’t miss your chance to explore how ML and LLM approaches can transform firmware analysis and learn how to leverage data-driven techniques to improve accuracy, scalability, and insight generation.

Can’t join live? No problem — register now, and you’ll receive the on-demand recording after the webinar.