Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation | Paper Citation Record

Observation dca280f3-1e80-4aeb-9f2c-a12af1e6cf8b · outbound

This paper cites The obfuscation executive.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation The obfuscation executive

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.364996Z

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No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 47aeb769-c9de-42f9-be32-8bfeea6393cd · outbound

This paper cites Protecting software through obfuscation: Can it keep pace with progress in code analysis?Acm computing surveys (csur), 49(1):1–37.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Protecting software through obfuscation: Can it keep pace with progress in code analysis?Acm computing surveys (csur), 49(1):1–37

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.371744Z

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No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 82abecde-0c26-4026-a217-3c4c44bf748f · outbound

This paper cites Chosen-instruction attack against commercial code virtualization obfuscators.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Chosen-instruction attack against commercial code virtualization obfuscators

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.351678Z

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No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 087b04b5-f9ab-43ab-baec-cd989ec13f86 · outbound

This paper cites Coat: Code obfuscation tool to evaluate the performance of code plagiarism detection tools.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Coat: Code obfuscation tool to evaluate the performance of code plagiarism detection tools

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.338144Z

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No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 56757699-c7a1-4e3f-afc7-6e4a180f19e9 · outbound

This paper cites Malware obfuscation techniques: A brief survey.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Malware obfuscation techniques: A brief survey

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.341480Z

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No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 764ec926-a1d7-473f-8f04-5e4f5f978eb9 · outbound

This paper cites Binaryai: Binary software composition analysis via intelligent binary source code matching.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Binaryai: Binary software composition analysis via intelligent binary source code matching

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.348522Z

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Observation 3667af59-0231-414a-9cbe-dcd64bfc0a11 · outbound

This paper cites Poster: E-graphs and equality saturation for term-rewriting in mba deobfuscation: An empirical study.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Poster: E-graphs and equality saturation for term-rewriting in mba deobfuscation: An empirical study

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.361887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 1c1258e4-30cf-4f45-9eff-9f1ea4f5a71e · outbound

This paper cites Simplifying mixed boolean-arithmetic obfuscation by program synthesis and term rewriting.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Simplifying mixed boolean-arithmetic obfuscation by program synthesis and term rewriting

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.387735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 112100d1-b42f-4c1a-b8d5-b23c71984056 · outbound

This paper cites A generic approach to automatic deobfuscation of executable code.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation A generic approach to automatic deobfuscation of executable code

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.405041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 83c82cb0-3a38-4a0f-ba25-14d7d263d77c · outbound

This paper cites Sok: Automatic deobfuscation of virtualization-protected applications.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Sok: Automatic deobfuscation of virtualization-protected applications

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.427446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 12cccbf5-ca19-4f78-ba20-67079ded9650 · outbound

This paper cites Control-flow deobfuscation using trace-informed compositional program synthesis.Proceedings of the ACM on Programming Languages, 8(OOPSLA2):2211–2241.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Control-flow deobfuscation using trace-informed compositional program synthesis.Proceedings of the ACM on Programming Languages, 8(OOPSLA2):2211–2241

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.375032Z

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No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 48765808-45a4-48cd-be91-acb294d32720 · outbound

This paper cites {MBA-Blast}: Unveiling and simplifying mixed {Boolean-Arithmetic} obfuscation.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation {MBA-Blast}: Unveiling and simplifying mixed {Boolean-Arithmetic} obfuscation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.414724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ca947a54-9277-4cdf-b2f6-e476a832ff45 · outbound

This paper cites Llm-based test-driven interactive code generation: User study and empirical evaluation.IEEE Transactions on Software Engineering.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Llm-based test-driven interactive code generation: User study and empirical evaluation.IEEE Transactions on Software Engineering

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.394228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ccd68a47-1491-4b64-84dd-8702329e59f3 · outbound

This paper cites Automated program repair in the era of large pre-trained language models.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Automated program repair in the era of large pre-trained language models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.317838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation e1917105-7317-43e7-821f-ed7978aad7e2 · outbound

This paper cites an unresolved cited work.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-05-17T07:29:15.290642Z

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No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation cc5b2749-62b7-47f1-a626-abe217badbf1 · outbound

This paper cites Degpt: Optimizing decompiler output with llm.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Degpt: Optimizing decompiler output with llm

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.293754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 603ea3ee-8ee5-46c2-a8d0-97948ae3b995 · outbound

This paper cites How far have we gone in binary code understanding using large language models.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation How far have we gone in binary code understanding using large language models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.300355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation aead28a8-1b4f-43a7-bb43-eff7f85f694c · outbound

This paper cites BinMetric: A Comprehensive Binary Analysis Benchmark for Large Language Models.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation BinMetric: A Comprehensive Binary Analysis Benchmark for Large Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:36:00.019238Z

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No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation eb0f7e5d-b8f6-45ba-91f0-218143e2f5d9 · outbound

This paper cites LLM4Decompile: Decompiling Binary Code with Large Language Models.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation LLM4Decompile: Decompiling Binary Code with Large Language Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:36:00.007421Z

Source-reported events for the cited work

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Observation 16d5036c-d990-4e24-8d8c-953fd56fd234 · outbound

This paper cites Beyond classification: Inferring function names in stripped binaries via domain adapted llms.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Beyond classification: Inferring function names in stripped binaries via domain adapted llms

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.296906Z

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No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 05016afd-6672-4083-87de-11e375547896 · outbound

This paper cites Misum: Multi-modality heterogeneous code graph learning for multi-intent binary code summarization.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Misum: Multi-modality heterogeneous code graph learning for multi-intent binary code summarization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.310709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 2d45ae57-331b-41ee-a0c7-ea58be94c248 · outbound

This paper cites Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:35:59.800329Z

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No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 8f4dfaaf-7b78-498c-b12c-9933cb06e4dd · outbound

This paper cites Typeforge: Synthesizing and selecting best-fit composite data types for stripped binaries.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Typeforge: Synthesizing and selecting best-fit composite data types for stripped binaries

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.327944Z

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No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation e118b5a3-aabe-47e4-8a6b-95436d0a5b2a · outbound

This paper cites Qwen2.5-Coder Technical Report.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Qwen2.5-Coder Technical Report

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-11T05:36:00.060188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation fa6700bc-b2d0-4e33-80d4-3302121670e1 · outbound

This paper cites Qwen3 technical report.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Qwen3 technical report

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.331441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 48ea9381-510e-4f1f-9487-7a3e5153b464 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Code Llama: Open Foundation Models for Code

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-11T05:35:59.893333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ee1530bb-d320-48bd-8f84-ae70418e2e91 · outbound

This paper cites Introducing llama 3.1: Our most capable models to date.https://ai.meta.com/blog/meta-llama-3-1/.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Introducing llama 3.1: Our most capable models to date.https://ai.meta.com/blog/meta-llama-3-1/

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.421265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 8fd89c60-6602-49a3-95a3-d2716f29a944 · outbound

This paper cites DeepSeek-V3 Technical Report.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation DeepSeek-V3 Technical Report

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-11T05:36:00.066930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 1100a51b-f12e-448a-8eb5-3171beb54451 · outbound

This paper cites Hello gpt-4 turbo.https://openai.com/index/hello-gpt-4o/.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Hello gpt-4 turbo.https://openai.com/index/hello-gpt-4o/

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.324731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 86e3e252-31b9-4b3c-9273-b80242332211 · outbound

This paper cites an unresolved cited work.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-05-17T07:29:15.430642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation a0ea83d3-fc8d-47da-a75a-114489d27c46 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-11T05:36:00.027039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation df626d15-6534-4e96-9872-ab9a3e1c997d · outbound

This paper cites ReCopilot: Reverse Engineering Copilot in Binary Analysis.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation ReCopilot: Reverse Engineering Copilot in Binary Analysis

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:35:59.789350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation f3cb200a-9d18-4beb-a975-bd0271347ae2 · outbound

This paper cites Chatdeob: An effective deobfuscation method based on large language model.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Chatdeob: An effective deobfuscation method based on large language model

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.238262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 12d2d871-15f8-455b-a640-45b2cf0c754a · outbound

This paper cites D810.https://github.com/joydo/d810.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation D810.https://github.com/joydo/d810

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.241913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation eb825e04-92ba-4db8-a932-61ff1563a0ae · outbound

This paper cites Goomba.https://hex-rays.com/blog/deobfuscation-with-goomba.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Goomba.https://hex-rays.com/blog/deobfuscation-with-goomba

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.249479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ea5a7fa8-cdbb-455a-ba40-2a0b5407980a · outbound

This paper cites PhD thesis, Université Grenoble Alpes.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation PhD thesis, Université Grenoble Alpes

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.268538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 8915e9d1-75ea-46ad-9e85-8284881abb0d · outbound

This paper cites Defeating opaque predicates statically through machine learning and binary analysis.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Defeating opaque predicates statically through machine learning and binary analysis

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.401998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 48cb422e-615a-4b85-b1e4-56d000175a2d · outbound

This paper cites X- mba: Towards heterogeneous mixed boolean-arithmetic deobfuscation.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation X- mba: Towards heterogeneous mixed boolean-arithmetic deobfuscation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.390683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation d1fa10f1-5ee0-4534-b843-411c19704513 · outbound

This paper cites Dose: Deobfuscation based on semantic equivalence.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Dose: Deobfuscation based on semantic equivalence

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.397376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 26a83175-fd22-46e2-9b97-28a912989ed5 · outbound

This paper cites Search-based local black-box deobfuscation: understand, improve and mitigate.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Search-based local black-box deobfuscation: understand, improve and mitigate

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.411532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b2643483-c6af-4859-a51f-8a00a768b6f6 · outbound

This paper cites Input-output example-guided data deobfuscation on binary.Security and Communication Networks, 2021(1):4646048.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Input-output example-guided data deobfuscation on binary.Security and Communication Networks, 2021(1):4646048

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.378116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation e3792e52-a0f3-439f-9d21-f2a7ae79849c · outbound

This paper cites Qsynth-a program synthesis based approach for binary code deobfuscation.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Qsynth-a program synthesis based approach for binary code deobfuscation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.384477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b66c14cf-6c12-4368-a4bb-71b0d49cdef5 · outbound

This paper cites Syntia: Synthesizing the semantics of obfuscated code.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Syntia: Synthesizing the semantics of obfuscated code

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.354919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 0481b583-6719-4c62-b00d-75b8e4f7e9a6 · outbound

This paper cites Seead: A semantic-based approach for automatic binary code de-obfuscation.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Seead: A semantic-based approach for automatic binary code de-obfuscation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.344758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation f0b4bced-0843-40a3-83aa-c86c29371a77 · outbound

This paper cites Exploring the potential of llms for code deobfuscation.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Exploring the potential of llms for code deobfuscation

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.358377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 7c1e74ce-d4ef-463e-9dac-363de1ef9275 · outbound

This paper cites Dobf: A deobfuscation pre-training objective for programming languages.Advances in Neural Information Processing Systems, 34:14967– 14979.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Dobf: A deobfuscation pre-training objective for programming languages.Advances in Neural Information Processing Systems, 34:14967– 14979

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.368472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation bda1e96f-5266-4cf4-aac6-14fc666ca615 · outbound

This paper cites Alfredo: Agentic llm-based framework for code deobfuscation.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Alfredo: Agentic llm-based framework for code deobfuscation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.381454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ebe0c1e3-cf55-4d8e-8fee-1d00b54f1d0c · outbound

This paper cites Can llms obfuscate code? a systematic analysis of large language models into assembly code obfuscation.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Can llms obfuscate code? a systematic analysis of large language models into assembly code obfuscation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.408451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 94586ee6-d2d2-4123-bf60-2ae23711965e · outbound

This paper cites Deconstructing Obfuscation: A four-dimensional framework for evaluating Large Language Models assembly code deobfuscation capabilities.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Deconstructing Obfuscation: A four-dimensional framework for evaluating Large Language Models assembly code deobfuscation capabilities

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:35:59.854939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 5c0f72ba-906b-427e-8187-458bcda2d815 · outbound

This paper cites Enabling obfuscation detection in binary software through explainable ai.IEEE Transactions on Emerging Topics in Computing.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Enabling obfuscation detection in binary software through explainable ai.IEEE Transactions on Emerging Topics in Computing

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.418228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b0707d89-0dfa-43eb-b98c-575d56e24048 · outbound

This paper cites Debra: A real-world benchmark for evaluating deobfuscation methods.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Debra: A real-world benchmark for evaluating deobfuscation methods

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.334810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 351e19d1-5232-4782-a5ba-5ce0d798ce43 · outbound

This paper cites Predicting the resilience of obfuscated code against symbolic execution attacks via machine learning.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Predicting the resilience of obfuscated code against symbolic execution attacks via machine learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.303343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 3f261d34-bc02-457c-aa6b-ccd77af14eb3 · outbound

This paper cites Mibench: A free, commercially representative embedded benchmark suite.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Mibench: A free, commercially representative embedded benchmark suite

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.306749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 0a61975c-8756-4d89-b981-adec8dd89e0d · outbound

This paper cites Spec cpu2006 benchmark descriptions.ACM SIGARCH Computer Architecture News, 34(4):1–17.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Spec cpu2006 benchmark descriptions.ACM SIGARCH Computer Architecture News, 34(4):1–17

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.314494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 4fc8505f-fe75-4d37-9afa-3a107f7b87bd · outbound

This paper cites An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:35:59.866085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 56bf8115-306d-4d79-aa2f-597359ace707 · outbound

This paper cites Bleu: a method for automatic evaluation of machine translation.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Bleu: a method for automatic evaluation of machine translation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.284193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ab886d88-dc80-4cdd-8dd4-c325080fe92d · outbound

This paper cites CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks

Reference 57

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T05:35:59.882422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 57a2b79f-7fa1-4aeb-abec-ea8994b451a5 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.280883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation fa3fc649-1476-4912-b665-871a4a9b8307 · outbound

This paper cites an unresolved cited work.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-05-17T07:29:15.274472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 3b428935-2997-40bd-be7f-3b219c89c931 · outbound

This paper cites CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-15T11:40:02.914862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation f483e0ad-a125-4269-8f8c-dd3052ae7bf0 · outbound

This paper cites CodeSearchNet Challenge: Evaluating the State of Semantic Code Search.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-12T16:06:20.913961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ccda8382-ca47-487a-bb05-81d4df170b2e · outbound

This paper cites Ollvm.https://github.com/obfuscator-llvm/obfuscator.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Ollvm.https://github.com/obfuscator-llvm/obfuscator

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.234330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation af5ac110-825f-42a0-bf50-9d2f4b77a4d5 · outbound

This paper cites Hikari.https://github.com/HikariObfuscator/Hikari.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Hikari.https://github.com/HikariObfuscator/Hikari

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.271639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation c6dedf1d-7669-4b2d-9592-db1086fdc80a · outbound

This paper cites Tigress.https://tigress.wtf.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Tigress.https://tigress.wtf

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.277430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 0011cedb-2733-4327-a2d1-8e6adfe2f107 · outbound

This paper cites Alcatraz.https://github.com/weak1337/Alcatraz.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Alcatraz.https://github.com/weak1337/Alcatraz

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.287629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 2d50f24d-24d5-4d1e-af83-ebd7e4ca0838 · outbound

This paper cites Loop: Logic-oriented opaque predicate detection in obfuscated binary code.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Loop: Logic-oriented opaque predicate detection in obfuscated binary code

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.321645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 8d44723c-9219-450e-8d88-7207d6a2cd77 · outbound

This paper cites Measuring nominal scale agreement among many raters.Psychological bulletin, 76(5):378.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Measuring nominal scale agreement among many raters.Psychological bulletin, 76(5):378

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.424068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation c585c445-2fda-4720-b289-06a35d57557d · outbound

This paper cites thezoo.https://github.com/ytisf/theZoo.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation thezoo.https://github.com/ytisf/theZoo

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.262392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 3caf8ddb-b936-4dad-9b5f-e8e5491679e0 · outbound

This paper cites Malwaresourcecode.https://github.com/vxunderground/MalwareSourceCode.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Malwaresourcecode.https://github.com/vxunderground/MalwareSourceCode

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.259224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation e1d2b2f4-3a0a-4f7e-8e06-a229c1830880 · outbound

This paper cites A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends

Reference 70

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T05:35:59.832296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 30fbb511-6df8-4cb6-b0a1-871a6e6b8cdd · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-05-11T05:35:59.823401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 777a1a44-0aca-47e2-95f0-03f9da45ac30 · outbound

This paper cites an unresolved cited work.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-05-17T07:29:15.265466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation d3542ab1-a2b6-4bfb-bd69-0c5426622ea2 · outbound

This paper cites LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:36:00.039662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 734aadf8-4b22-4178-b96b-50b706dd49ab · outbound

This paper cites When Text Embedding Meets Large Language Model: A Comprehensive Survey.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation When Text Embedding Meets Large Language Model: A Comprehensive Survey

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:36:00.086107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation db57d962-9e71-4d4e-b5d6-c54096418d00 · outbound

This paper cites CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval

Reference 75

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T05:35:59.842028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 3e02484e-188c-4cda-908a-7c11591cbfbf · outbound

This paper cites Efficient Code Embeddings from Code Generation Models.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Efficient Code Embeddings from Code Generation Models

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:35:59.874158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 3ed12db4-d75a-45e9-9759-9371e2e0d42a · outbound

This paper cites Qwen2 Technical Report.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Qwen2 Technical Report

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-05-11T05:36:00.074474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 092a029a-ab5f-41d0-88f1-a1f714408fb1 · outbound

This paper cites Cambridge university press.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Cambridge university press

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.252715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b3e4f36b-f398-41e9-8e78-86c7df9271d8 · outbound

This paper cites The dimensionality of program complexity.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation The dimensionality of program complexity

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.255991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 37478860-cbb1-45c5-82dc-62455225e3bb · outbound

This paper cites Software complexity analysis using halstead metrics.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Software complexity analysis using halstead metrics

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:15.245927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation d1317eab-aa51-4b8c-8c9e-c93f547f3190 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 81

Resolution: verified exact; local_arxiv, observed 2026-05-11T05:35:59.900904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers