VirusTotal Code Insight AI reversing pipeline demonstrates how large language models can triage Apple binaries at global scale and surface malware that traditional signatures miss. VirusTotal Code Insight ran a stress test on nearly 10,000 first-seen Mach-O binaries in a single day, analyzing only raw code without metadata or prior detections. The system flagged 164 binaries as malicious, compared to 67 detections from 70-plus conventional antivirus engines at the same time, and identified cases where Microsoft and ClamAV had generated false positives. VirusTotal Code Insight uncovered previously undetected threats, including a multi-stage macOS dropper that fetched AppleScript payloads from foggydoxz[.]xyz and an iOS jailbreak tweak that injected fake login prompts into Adobe Lightroom, exfiltrating credentials via an obfuscated Telegram bot. In both examples, AI-generated descriptions accurately mapped behavior such as C2 communication, persistence mechanisms, and phishing overlays, enabling human analysts to validate and extend findings with targeted manual reversing rather than starting from scratch. For SOCs and threat-research teams, VirusTotal Code Insight illustrates a practical division of labor: AI applies consistent, explainable reasoning across millions of binaries, while humans focus on gray-area cases and high-impact campaigns. The approach does not replace heuristic or ML-based detection but augments them with semantic analysis that can contextualize ambiguous samples and reduce alert fatigue from false positives.
🎯CORTEX Protocol Intelligence Assessment
Business Impact: VirusTotal Code Insight shows that AI-assisted reversing can reduce dwell time for novel Mac and iOS malware targeting enterprise endpoints, closing gaps left by signature lag and inconsistent static analysis. Organizations relying heavily on Apple ecosystems should incorporate similar approaches into threat-hunting and vendor evaluation strategies. Technical Context: VirusTotal Code Insight uses a pruning pipeline built on tools like Binary Ninja HLIL to extract key functions, strings, and imports from Mach-O binaries before feeding summaries into large language models. The system generates human-readable reports that highlight behaviors such as network exfiltration, credential theft, or injection, which can be turned into new detection rules and YARA signatures. Security teams can emulate this pattern by combining disassembly tooling with LLM reasoning in controlled workflows.
⚡Strategic Intelligence Guidance
- Prioritize Mac and iOS telemetry collection and integrate Apple-specific malware detections into enterprise EDR and SIEM platforms.
- Experiment with AI-assisted reversing workflows that summarize binaries and scripts before analysts perform deep dives.
- Translate AI-generated behavioral descriptions into concrete detection content such as YARA rules and endpoint analytics.
- Establish review procedures to validate AI findings, ensuring that automated triage does not introduce new classes of false positives into SOC pipelines.
Vendors
VirusTotalGoogleMicrosoftApple
Threats
MacOS malwareiOS credential stealers
Targets
Apple endpointsThreat research teams
Impact
Data Volume:9,981 Mach-O binaries in test run