# Malaika: Understanding Malware through Tri-Grounded Agentic Reasoning

arXiv: https://arxiv.org/abs/2607.09179 · HTML: https://mlsanity.com/paper/2607.09179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 10 Jul 2026.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 15 of 15)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks](https://mlsanity.com/paper/2602.12670/) (2026)
- [LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning](https://mlsanity.com/paper/2401.16185/) (2024)
- [PatchPilot: A Cost-Efficient Software Engineering Agent with Early Attempts on Formal Verification](https://mlsanity.com/paper/2502.02747/) (2025)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)](https://mlsanity.com/paper/2402.01359/) (2024)
- [TIF: Learning Temporal Invariance in Android Malware Detectors](https://mlsanity.com/paper/2502.05098/) (2025)
- [Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing](https://mlsanity.com/paper/2509.14335/) (2025)
- [Identifying Adversary Tactics and Techniques in Malware Binaries with an LLM Agent](https://mlsanity.com/paper/2602.06325/) (2026)
- [MARD: A Multi-Agent Framework for Robust Android Malware Detection](https://mlsanity.com/paper/2604.25264/) (2026)
- [PromptAudit: Auditing Prompt Sensitivity in LLM-Based Vulnerability Detection](https://mlsanity.com/paper/2605.24171/) (2026)
- [On the Reliability and Stability of Selective Methods in Malware Classification Tasks](https://mlsanity.com/paper/2505.22843/) (2025)
- [Trident: Improving Malware Detection with LLMs and Behavioral Features](https://mlsanity.com/paper/2605.00297/) (2026)
- [Veritas: Grounding LLM Agents for Reliable Vulnerability Reasoning over Stripped Binaries](https://mlsanity.com/paper/2605.15097/) (2026)
- [Evaluating LLMs for Obfuscation Detection and Classification in Android Apps](https://mlsanity.com/paper/2606.14233/) (2026)

## Abstract (arXiv)

Recent LLM-based systems have shown promising capabilities for security-focused code analysis. Malware understanding, however, poses a distinct challenge: analysts must reconstruct high-level malicious behaviors under partial observability from sparse, dispersed evidence intertwined with benign functionality. While static analysis can expose security-relevant signals, the central challenge is not merely identifying suspicious code, but determining whether the evidence sufficiently supports an auditable behavior-level conclusion. We formulate malware understanding as a grounded reasoning problem and argue that reliable behavior reconstruction requires three complementary forms of grounding. Domain grounding constrains how behavior hypotheses are generated and evaluated, semantics grounding localizes and connects supporting program evidence, and knowledge grounding supports behavioral attribution through externally verifiable threat knowledge. To study this hypothesis, we present Malaika, a multi-agent framework that operationalizes the three grounding mechanisms through analyst-inspired reasoning, tool-mediated evidence localization, and retrieval-based behavioral attribution. We instantiate Malaika for Android malware analysis and evaluate it on malware-understanding tasks. Results show that Malaika improves analysis quality over prior LLM-based malware-analysis frameworks and demonstrate that reliability depends not only on model capability but also on the reasoning process. In particular, comparisons against malware-analysis systems and frontier agentic frameworks show that grounding-aware reasoning produces more precise and auditable conclusions. Ablation studies further support the grounding hypothesis. These findings suggest that grounding-aware reasoning provides a principled foundation for reliable malware understanding and, more broadly, for evidence-grounded software analysis.