# AI-Powered Malware Analysis: Detecting Persistent Threats on Sensors

> An analysis using Gemma4 with Ollama reveals high-volume malware downloads on DShield sensors, indicating persistent actor activity and critical compromise risks.

- Published: 2026-08-13T09:04:55.000Z
- Severity: medium
- Category: Threat Intel
- Tags: AI, Malware Analysis, Threat Hunting, Botnet, Cowrie Sensor
- Author: Runtime Rebel Intel
- Primary source: https://isc.sans.edu/diary/rss/33242
- Canonical: https://runtimerebel.com/blog/ai-powered-malware-analysis-detecting-persistent-threats-on-sensors

## Key points

- High-volume file downloads on DShield/Cowrie sensors indicate persistent actor activity and potential compromise.
- Observed on DShield/Cowrie honeypots, with implications for enterprise network security and detection capabilities.
- Isolate compromised hosts immediately, enhance logging, and conduct enterprise-wide threat hunting for observed hashes.

## [AI](/glossary#ai)-Powered Analysis Uncovers Persistent Threat Activity on DShield Sensors

Runtime Rebel's latest intelligence highlights an analytical exercise leveraging Gemma4 as a Large Language Model ([LLM](/glossary#jailbreak-llm)) with Ollama to scrutinize [malware](/glossary#malware) hashes collected by DShield sensors. This effort, documented by [SANS ISC](https://isc.sans.edu/diary/rss/33242), aims to assess the utility of AI in identifying indicators of compromise (IoCs), understanding threat tactics, and providing actionable recommendations for defense. The findings reveal significant, high-volume malicious activity on these interactive honeypots, pointing to established actor [persistence](/glossary#persistence) and the continuous delivery of payloads.

### Technical Details: Analyzing Malware Hashes with AI

The analysis focused on malware hashes uploaded to the DShield sensor over a 30-day period, comparing data against VirusTotal and CyberGordon. Gemma4 was tasked with determining concerns related to volumes, matching IoCs to external intelligence, providing comparative analysis, identifying malware families, and highlighting potential issues. The model, specifically `gemma4:e4b`, revealed crucial insights into observed behaviors:

#### Volume, IoCs, and Tactics

The most pressing concern identified was the sheer volume of downloaded hashes (126, 110 events) on Cowrie sensors. While high volumes alone don't confirm malicious intent, their occurrence on an interactive sensor strongly indicates successful actor persistence and attempts at [data exfiltration](/glossary#data-exfiltration). The hashes themselves act as IoCs, but the absence of direct matches on external sites shifted the focus to behavioral analysis.

The activity observed strongly suggests [MITRE ATT&CK](/glossary#mitre-att-ck) tactics such as **Persistence**, **Command and Control ([C2](/glossary#c2))** communication, and potentially **[Lateral Movement](/glossary#lateral-movement)/Exfiltration**. The repeated downloading of specific files, rather than mere system queries, signifies that actors have established a foothold and are actively operating within the compromised environment.

#### Comparative Analysis of [Threat Intelligence](/glossary#threat-intelligence) Sources

Gemma4's comparative analysis of VirusTotal (VT) and CyberGordon highlighted their respective strengths and weaknesses for **malware analysis and [threat hunting](/glossary#threat-hunting)**. VirusTotal was deemed superior for immediate threat context due to its high-fidelity, integrated threat intelligence from multiple [AV](/glossary#anti-virus-av) engines. CyberGordon, offering academic/historical analysis, proved useful for deeper source tracking and niche observations. A critical finding was the lack of actionable intelligence from the provided snippets of both platforms, emphasizing the need for a structured workflow to ensure full processing of results for all observed hashes.

#### Identifying Malware Families from Behavioral Patterns

Given the lack of specific threat intelligence results for individual hashes, Gemma4's analysis of the top three hashes was based on observed high-volume command-and-control behavior:

*   **Hash 1 (197c74408e15bd1168105f564f96aace4fd4819961b724630bf5a6be4878daf8):** Classified as **[Botnet](/glossary#botnet)/Loader Malware**. The extremely high event count and consistent downloading pattern are classic indicators of a [payload](/glossary#payload) repeatedly delivered by an established botnet or [initial access](/glossary#initial-access) Trojan ([RAT](/glossary#remote-access-trojan-rat)).
*   **Hash 2 (31d4181843b1ed10a7e7cb3f108f6d6c50a7a4452ee52ddacabe8ca77260615e):** Identified as **[Backdoor](/glossary#backdoor)/[Keylogger](/glossary#keylogger)**. Similar to the first, this hash likely represents a specific payload for data exfiltration, such as credentials or session tokens.
*   **Hash 3 (94f2e4d8d4436874785cd14e6e6d403507b8750852f7f2040352069a75da4c00):** Suggested to be **Credential Stealer/Dropper**. This could be a modular component or staged payload designed to drop final malware after initial access.

The primary issue across these top hashes is not just what they are, but that they are actively and repeatedly downloaded by controlled actors over a sustained period, confirming successful compromise and established persistence. This demonstrates that actors have found pathways to bypass existing security controls.

### Actionable Recommendations for Cowrie Sensor Compromise Prevention

Defenders should prioritize immediate and proactive measures based on these findings:

*   **Immediate Containment:** Treat any machine connected through the sensor as compromised. Isolate the host and initiate forensic imaging without delay.
*   **Mitigation at Sensor Level:** Enhance Cowrie's logging and alerting capabilities to track file *execution* attempts, not just downloads. Implement stricter [egress filtering](/glossary#egress-filtering) on the network hosting the sensors to prevent C2 communications and data exfiltration.
*   **Threat Hunting (Proactive):** Utilize the identified top 10 hashes to query internal [Endpoint](/glossary#endpoint) Detection and Response ([EDR](/glossary#edr)) solutions across the entire enterprise. Search for these hash signatures or file names on any system *outside* of the sensor environment to identify potential wider compromise.
*   **[Hardening](/glossary#hardening):** Review user access policies and [network segmentation](/glossary#network-segmentation) rules. The repeated downloading strongly suggests persistent gaps in perimeter controls that require immediate attention to prevent future incidents involving **detecting botnet loader malware** and similar threats.

**Related:** [Dolphin X Malware: AI-Driven Target Prioritization & Defense](/blog/dolphin-x-malware-ai-driven-target-prioritization-defense), [YARA-X 1.18.0 & 1.19.0 Release: Enhancing Malware Detection](/blog/yara-x-1-18-0-1-19-0-release-enhancing-malware-detection)

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