Dynamic Incident Response
Dynamic Incident Response
Multi-Agent Framework for Resilient Malware Analysis
Malware analysis involves examining a program’s binary or source artefacts without execution, with the objective of understanding its structure, behaviour, and potential capabilities. Analysts typically begin by parsing the file format and disassembling machine code into human-readable instructions. These instructions may then be lifted into intermediate representations (IR) such as Static Single Assignment (SSA) form, and further transformed into higher-level artefacts including control-flow graphs (CFGs), data-flow graphs, and call graphs.
However, conventional static analysis methods encounter substantial limitations when dealing with modern malware. alicious payloads are often encrypted or compressed, only to be unpacked during runtime, leaving static analysis with limited visibility of the true malicious content. This process is also inherently time-consuming, resource-intensive, and difficult to scale when rapid triage is required.
Intractābilis has developed an LLM-powered multi-agent framework capable of conducting advanced, large-scale malware analysis. The system ingests opaque binaries and produces de-obfuscated intermediate representations, detailed function- and program-level summaries, capability and TTP attributions, provenance and family clustering, as well as machine-verifiable artefacts and semantic invariants.
This solution strengthens the capabilities of security analysts by enabling them to manage the growing volume and complexity of malware more effectively. It delivers high-fidelity, structured intelligence suitable for triage, detection, threat hunting, and broader intelligence workflows. By automating the routine and large-scale aspects of analysis, the framework allows human experts to focus on the most novel, evasive, or strategically significant threats.
Enhancing Power Grid Resilience Through Intelligent Threat Localisation
As modern power grids evolve into highly intelligent systems through the deployment of large numbers of smart devices—such as advanced meters used to monitor and control electricity flow—they become increasingly vulnerable to cyber-physical attacks. In such scenarios, adversaries target both the digital infrastructure and the physical components of the grid simultaneously. These attacks are often easier for the perpetrator to execute, as they demand less detailed insider knowledge of grid operations. For operators, the critical challenge lies in rapidly and accurately determining both the location and nature of faults arising from such events.
Intractābilis has developed a Graph Attention Network protocol combined with Federated Learning to assess the structural characteristics of the power grid and estimate the likelihood of physical damage to individual transmission lines within affected areas. By producing highly granular likelihood scores, the system enables fault diagnosis to be performed with greater speed and accuracy, even under complex and coordinated attack conditions.
This approach applies state-of-the-art machine learning to strengthen the operational resilience of power grids. It equips operators with enhanced detection and response capabilities, enabling them to counter sophisticated threats more effectively and safeguard the continuity of power delivery.
Together, these solutions form a robust, multi-layered threat response strategy that spans the physical, cyber-physical, and digital dimensions of modern infrastructure. We enable organisations to not only detect and analyse threats faster but to respond with precision, maintain operational integrity, and build resilience into every layer of their systems. Ready to Work Together?
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