AI agents can help analysts inspect malware and reverse engineer binaries, but current evaluations do not show that they can reliably do the work autonomously. On Columbia University DAPLab’s SRE-Bench, the strongest evaluated agent fully solved 31.5% of benchmark programs. Separate evaluations find significant gaps in malware analysis and binary comprehension. The practical role today is assistance: agents can organize and accelerate parts of an investigation, while analysts verify the evidence and conclusions.
What makes malware reverse engineering difficult for AI agents?
Reverse engineering a binary is different from analyzing readable source code. Defenders often receive executable files without the original source, so an analyst must infer what a program does from its compiled instructions, behavior, and supporting evidence. A fluent explanation is not proof that an agent has correctly understood the program.
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Benchmark design matters, too. A model might recognize a previously published challenge instead of independently analyzing it. Results also depend on the binary format, available tools, call or token budgets, scoring method, and whether a benchmark measures a complete solution or partial progress. A score from one evaluation therefore cannot be treated as a universal rating of an agent’s malware-analysis ability.
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Evaluations published or reported by their project authors in 2026 test different parts of the problem. Their results point to useful but incomplete capabilities, not to established real-world malware detection accuracy.
#1 Best Overall
| Evaluation | What it tests | Reported result | What the result indicates |
|---|---|---|---|
| SRE-Bench, Columbia University DAPLab | Reverse engineering realistic-scale binaries, with contamination controls and deterministically graded tasks. | The benchmark contains 262 binary instances and 1,572 tasks. Its project page reports a top evaluated agent score of 61.4% per instance and a 31.5% full-solve rate. | Agents can make substantial progress, but full completion remains difficult in this benchmark. These figures are not estimates of real-world malware detection accuracy. |
| AgentRE-Bench | Structured analysis of unfamiliar ELF and PE binaries using a fixed static-analysis toolset and deterministic scoring. | In its August 2026 Windows V3 evaluation of 20 artifacts, the best reported total was 1.3618 out of 2.0; no model recovered the bonus-level C2 endpoint. | This bounded benchmark shows that models can earn partial credit without recovering every target finding. Its score is not directly comparable with SRE-Bench’s solve rate. |
| CyberSOCEval, Meta | Malware analysis and threat-intelligence reasoning. | Meta reports that current LLMs remain far from saturating the evaluations. | Performance on security-analysis tasks remains incomplete. The result does not establish that reasoning models are categorically ineffective. |
| Reveree | Reverse-engineering challenges, measured through solve rate, progress across an eight-stage process schema, and agent actions. | The authors evaluated nine frontier models and four prompting strategies on 88 picoCTF and NYU-CTF challenges. They report that failures cluster in comprehension stages. | Looking only at final answers can hide where an agent gets stuck. The study also found that memorization and genuine analysis can both contribute to answers. |
These results answer different questions. SRE-Bench emphasizes realism, program scale, and contamination control; AgentRE-Bench uses bounded tasks, a fixed toolset, and deterministic scoring; CyberSOCEval covers malware and threat-intelligence reasoning; and Reveree examines both outcomes and process. The reported numbers should not be ranked as if they came from one shared test.
Can an agent fully reverse engineer a malware binary?
It may solve some tasks or identify useful findings, but the cited evaluations do not establish dependable end-to-end performance. SRE-Bench’s 31.5% full-solve rate is especially useful context: a strong score on individual tasks does not mean an agent consistently completes an entire program analysis.
SRE-Bench was built from 19 private programs averaging 16,900 lines of code, with 44 anti-analysis primitives. Its authors report that construction involved more than 5,000 expert hours. The benchmark includes six difficulty levels. Its project page also reports that agents were relatively insensitive to compiler optimization and static linking, which the authors interpret as potentially reflecting pattern matching rather than human-like semantic reasoning. These are benchmark-specific observations, not proof of how an agent will perform on an arbitrary specimen.
AgentRE-Bench offers a more bounded view: its repository describes 23 levels, comprising 13 Linux ELF x86-64 binaries and 10 Windows PE32+ binaries. In the reported Windows V3 results, the top total remained below the 2.0 maximum and no model recovered the bonus-level C2 endpoint. That indicates partial success on that particular synthetic test, not a general inability to find command-and-control infrastructure.
Rank #3
How well do agents understand what they analyze?
Reveree’s process-level analysis suggests that comprehension—not simply persistence or willingness to use tools—can be a major bottleneck. In its evaluation, failures concentrated in comprehension stages, and extra budget, persistence, or reasoning effort rescued few failures. More time or longer reasoning may help in some cases, but the study does not support treating either as a reliable fix for misunderstanding a binary.
The same work found that models could reproduce picoCTF flags from challenge descriptions alone, while measurable recall was minimal on NYU-CTF. The authors’ results suggest that remembered answers and genuine analysis can coexist. This is why a correct-looking output should be checked against the binary and the intermediate evidence rather than accepted on fluency or confidence.
What are the risks of using an agent on a suspicious binary?
A 2026 proof-of-concept study describes a possible prompt-injection path: malicious strings embedded in a binary can influence LLM-powered disassembly or decompilation systems and lead them to misinterpret code. The study establishes a possible attack route, not how often it occurs in deployed tools. It is still a reason to treat content extracted from a sample as untrusted.
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- Preserve tool output and intermediate findings so an analyst can trace how the agent reached a claim.
- Check consequential conclusions against the binary and relevant tool evidence; do not rely on a summary alone.
- Require qualified human review before using an agent’s output to make incident conclusions or consequential blocking decisions.
How should defenders judge claims about AI malware analysis?
When a benchmark result or product claim says an agent can analyze malware, ask what was actually tested. A result is easier to interpret when it identifies the binary format and scale, available tools and budgets, how prior exposure was controlled, how answers were scored, and whether the task involved malware classification, reverse engineering, or threat-intelligence reasoning.
Best Value
Also ask whether the reported measure is a full-task completion rate, partial credit, progress through analysis stages, or answer quality. Those measures reveal different strengths and failure modes. Without that context, a single percentage says little about whether an agent can safely support a particular investigation.
What is the practical role for AI agents now?
The evidence supports using agents as analyst aids, not as autonomous authorities. They may speed up selected parts of an investigation, particularly when paired with static-analysis tools, but their findings need to remain traceable and independently checked. The benchmark literature does not establish production performance, the prevalence of agent use in security operations, or real-world malware detection accuracy.
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