RINSER: Accurate API Prediction Using Masked Language Models

• URL: http://arxiv.org/abs/2509.04887v1
• Date: Fri, 05 Sep 2025 08:08:11 GMT
• Title: RINSER: Accurate API Prediction Using Masked Language Models
• Authors: Muhammad Ejaz Ahmed, Christopher Cody, Muhammad Ikram, Sean Lamont, Alsharif Abuadbba, Seyit Camtepe, Surya Nepal, Muhammad Ali Kaafar,
• Abstract summary: We present RINSER, an automated framework for predicting Windows API (WinAPI) function names.<n>RINSER relies on BERT's masked language model (LM) to predict API names at scale.<n>We evaluate RINSER on a large dataset of 4.7M API codeprints from 11,098 malware binaries, covering 4,123 unique Windows APIs.
• Score: 21.081906052711172
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Malware authors commonly use obfuscation to hide API identities in binary files, making analysis difficult and time-consuming for a human expert to understand the behavior and intent of the program. Automatic API prediction tools are necessary to efficiently analyze unknown binaries, facilitating rapid malware triage while reducing the workload on human analysts. In this paper, we present RINSER (AccuRate API predictioN using maSked languagE model leaRning), an automated framework for predicting Windows API (WinAPI) function names. RINSER introduces the novel concept of API codeprints, a set of API-relevant assembly instructions, and supports x86 PE binaries. RINSER relies on BERT's masked language model (LM) to predict API names at scale, achieving 85.77% accuracy for normal binaries and 82.88% accuracy for stripped binaries. We evaluate RINSER on a large dataset of 4.7M API codeprints from 11,098 malware binaries, covering 4,123 unique Windows APIs, making it the largest publicly available dataset of this type. RINSER successfully discovered 65 obfuscated Windows APIs related to C2 communication, spying, and evasion in our dataset, which the commercial disassembler IDA failed to identify. Furthermore, we compared RINSER against three state-of-the-art approaches, showing over 20% higher prediction accuracy. We also demonstrated RINSER's resilience to adversarial attacks, including instruction randomization and code displacement, with a performance drop of no more than 3%.

Related papers

• Framework-Aware Code Generation with API Knowledge Graph-Constructed Data: A Study on HarmonyOS [52.483888557864326]
  APIKG4SYN is a framework designed to exploit API knowledge graphs for the construction of API-oriented question-code pairs.<n>We build the first benchmark for HarmonyOS code generation using APIKG4SYN.
  arXiv  Detail & Related papers  (2025-11-29T08:13:54Z)
• Differentially Private Synthetic Data Release for Topics API Outputs [63.79476766779742]
  We focus on one Privacy-Preserving Ads API: the Topics API, part of Google Chrome's Privacy Sandbox.<n>We generate a differentially-private dataset that closely matches the re-identification risk properties of the real Topics API data.<n>We hope this will enable external researchers to analyze the API in-depth and replicate prior and future work on a realistic large-scale dataset.
  arXiv  Detail & Related papers  (2025-06-30T13:46:57Z)
• OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents [60.78202583483591]
  We introduce OS-Harm, a new benchmark for measuring safety of computer use agents.<n> OS-Harm is built on top of the OSWorld environment and aims to test models across three categories of harm: deliberate user misuse, prompt injection attacks, and model misbehavior.<n>We evaluate computer use agents based on a range of frontier models and provide insights into their safety.
  arXiv  Detail & Related papers  (2025-06-17T17:59:31Z)
• Malware Detection based on API calls [0.48866322421122627]
  We explore a lightweight, order-invariant approach to detecting and mitigating malware threats.<n>We publish a public dataset of over three hundred thousand samples, annotated with labels indicating benign or malicious activity.<n>We leverage machine learning algorithms, such as random forests, and conduct behavioral analysis by examining patterns and anomalies in API call sequences.
  arXiv  Detail & Related papers  (2025-02-18T13:51:56Z)
• Mitigating the Impact of Malware Evolution on API Sequence-based Windows Malware Detector [5.953199557879621]
  Methods based on API sequences play a crucial role in malware prevention. Evolved malware samples often use the API sequences of the pre-evolution samples to achieve similar malicious behaviors. We propose a frame(MME) framework that can enhance existing API sequence-based malware detectors.
  arXiv  Detail & Related papers  (2024-08-03T04:21:24Z)
• A Classification-by-Retrieval Framework for Few-Shot Anomaly Detection to Detect API Injection Attacks [9.693391036125908]
  We propose a novel unsupervised few-shot anomaly detection framework composed of two main parts. First, we train a dedicated generic language model for API based on FastText embedding. Next, we use Approximate Nearest Neighbor search in a classification-by-retrieval approach.
  arXiv  Detail & Related papers  (2024-05-18T10:15:31Z)
• FoC: Figure out the Cryptographic Functions in Stripped Binaries with LLMs [51.898805184427545]
  We propose a novel framework called FoC to Figure out the Cryptographic functions in stripped binaries.<n>We first build a binary large language model (FoC-BinLLM) to summarize the semantics of cryptographic functions in natural language.<n>We then build a binary code similarity model (FoC-Sim) upon the FoC-BinLLM to create change-sensitive representations and use it to retrieve similar implementations of unknown cryptographic functions in a database.
  arXiv  Detail & Related papers  (2024-03-27T09:45:33Z)
• Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4 [45.935748395725206]
  We introduce a prompt engineering-assisted malware dynamic analysis using GPT-4. In this method, GPT-4 is employed to create explanatory text for each API call within the API sequence. BERT is used to obtain the representation of the text, from which we derive the representation of the API sequence.
  arXiv  Detail & Related papers  (2023-12-13T17:39:44Z)
• Private-Library-Oriented Code Generation with Large Language Models [52.73999698194344]
  This paper focuses on utilizing large language models (LLMs) for code generation in private libraries. We propose a novel framework that emulates the process of programmers writing private code. We create four private library benchmarks, including TorchDataEval, TorchDataComplexEval, MonkeyEval, and BeatNumEval.
  arXiv  Detail & Related papers  (2023-07-28T07:43:13Z)
• Evaluating Embedding APIs for Information Retrieval [51.24236853841468]
  We evaluate the capabilities of existing semantic embedding APIs on domain generalization and multilingual retrieval. We find that re-ranking BM25 results using the APIs is a budget-friendly approach and is most effective in English. For non-English retrieval, re-ranking still improves the results, but a hybrid model with BM25 works best, albeit at a higher cost.
  arXiv  Detail & Related papers  (2023-05-10T16:40:52Z)
• Simple Transparent Adversarial Examples [65.65977217108659]
  We introduce secret embedding and transparent adversarial examples as a simpler way to evaluate robustness. As a result, they pose a serious threat where APIs are used for high-stakes applications.
  arXiv  Detail & Related papers  (2021-05-20T11:54:26Z)
• Embedding Code Contexts for Cryptographic API Suggestion:New Methodologies and Comparisons [9.011910726620536]
  We present a new neural network-based approach, Multi-HyLSTM for API recommendation. We use program analysis to guide the API embedding and recommendation. In an analysis of 245 test cases, compared with the commercial tool Codota, we achieve a top-1 recommendation accuracy of 88.98%.
  arXiv  Detail & Related papers  (2021-03-15T22:27:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.