Multi-View Decompilation for LLM-Based Malware Classification

What Happened

A new preprint (arXiv:2606.20436) proposes a technique called "multi-view decompilation" to improve LLM-based malware classification. The core insight is that decompiling a binary into pseudo-C code—a standard practice for malware analysts—produces only one representation of the original program. The researchers argue that generating multiple decompilation views from the same binary, then feeding them to an LLM, yields more robust classification results than relying on a single decompilation pass.

The work addresses a practical bottleneck: current decompilers (e.g., IDA Pro, Ghidra) produce deterministic output, but different decompilation strategies or optimization levels can highlight different code patterns. By combining multiple views, the LLM gains a richer signal for distinguishing malicious from benign logic, potentially reducing false positives and missed detections.

Why It Matters

This research sits at the intersection of two trends: the growing use of LLMs in security workflows, and the persistent challenge of analyzing malware without source code. Traditional static analysis relies on handcrafted signatures or heuristics, which struggle with obfuscation and polymorphism. LLMs offer a more flexible approach, but they are sensitive to input representation—a single decompiled view might miss critical context that another view captures.

The multi-view approach is notable because it does not require retraining the LLM or building a custom model. Instead, it leverages the LLM's existing capabilities by providing it with complementary perspectives. This is analogous to how human analysts benefit from seeing both a high-level control flow graph and a detailed register trace.

For AI practitioners, the implication is clear: input engineering matters as much as model architecture. The same LLM can perform significantly better or worse on a task depending on how the data is structured and presented. This work suggests that for security-critical tasks, redundancy in input representation can compensate for the LLM's lack of deep binary understanding.

Implications for AI Practitioners

First, this technique is immediately applicable to anyone using LLMs for code analysis—not just malware classification. Code review, vulnerability detection, and decompilation-based debugging could all benefit from multi-view inputs. The overhead is minimal: generate multiple decompilation passes (e.g., with different decompiler settings or optimization flags) and concatenate or interleave them in the prompt.

Second, the approach highlights a broader principle: LLMs are pattern matchers, not semantic reasoners. When the "ground truth" is ambiguous (like a binary's intent), providing multiple valid interpretations helps the model converge on the correct classification. Practitioners should consider whether their own tasks benefit from similar multi-perspective prompting.

Third, there is a cost consideration. Multi-view decompilation increases token usage linearly with the number of views. For large-scale malware triage, this could become expensive. The research does not yet address whether two views are sufficient or whether more views yield diminishing returns—a practical trade-off that implementers will need to test.

Finally, this work reinforces that LLM-based security tools remain complementary to traditional methods. Multi-view decompilation improves classification accuracy, but it does not eliminate the need for sandboxing, dynamic analysis, or human expertise. The best systems will likely combine LLM-driven static analysis with other detection layers.

Key Takeaways

• Multi-view decompilation improves LLM malware classification by providing the model with multiple representations of the same binary, reducing reliance on any single decompiler output.
• The technique is model-agnostic and requires no retraining, making it easy to integrate into existing LLM-based security pipelines.
• Practitioners should consider multi-view prompting for other code analysis tasks, but must account for increased token costs and potential diminishing returns.
• LLM-based static analysis remains a complement to, not a replacement for, traditional malware detection methods.