Skip to content
Preprint

Aray: Deterministic-First Synthesis of Benign Artifacts for YARA Validation

Aug 2026 · 0 citations · 23 references
Computer Science

TL;DR

This work presents Aray, a deterministic-first YARA interpreter and positive-fixture synthesizer, a deterministic-first YARA interpreter and positive-fixture synthesizer that validates generated fixtures against their source rules.

Abstract

A YARA rule is easy to distribute, but the malware sample used to demonstrate a positive match is not. This complicates storage, continuous integration, disaster-recovery exercises, and reproducible scanner validation. Constructing a replacement fixture requires more than embedding literals: YARA conditions can combine alternatives, counts, offsets, integer reads, and executable-container constraints, while the resulting file should not reproduce malware behavior. Positive validation is existential: it requires one file-level member of a rule's match set, not reconstruction of the originating sample. We present Aray, a deterministic-first YARA interpreter and positive-fixture synthesizer. Models may propose constructive normalizations or typed extraction fallbacks, but never backend source or binary structure. Conventional code validates normalized rules, derives string and integer witnesses, and performs extraction, routing, collision-checked layout, and ELF, PE, or generic serialization. Only residual normalization semantics reach a bounded model judge. We evaluated Aray over 416 public-rule entries. Normalization accepted 182 entries without model assistance and 234 after model normalization. Constructibility preflight admitted 406 entries, and every admitted fixture matched its upstream original rule. This yields 406/416 (97.6%) overall and 406/406 among constructible rules, with ten expected preflight dispositions and no scanner mismatches or construction failures. An unreachable endpoint confirmed zero model invocations during realization. The original-rule oracle validates generated fixtures against their source rules; proving implication for all possible files is a separate, stronger objective. Two anchored-regex failures were repaired before the final run, so these are post-fix systems results, not a held-out estimate.

View source

Similar papers

Open access Aug 2026

Real-Time Detection and Mitigation of Prompt Injection Attacks in LLM-Integrated Enterprise Systems

Large language models (LLMs) embedded in enterprise workflows cannot structurally distinguish legitimate instructions from adversarial ones in the same token stream, making prompt injection OWASP's top LLM risk for two consecutive editions a persistent threat across direct and indirect vectors. This paper presents Prom...

Fatimah Alhamzawi · 0 citations
Preprint Aug 2026

CodeMechanic: Bug-Property-Guided Program Mitigation

This work presents CodeMechanic, a bug-property-guided system for generating constrained mit- igations for spatial memory corruption that combines a two-dimensional static and dynamic context extractor with in-prompt debugging knowledge and stepwise val- idation to limit the effect of LLM errors.

Han Zheng, Rafaila Galanopoulou, Ilia Shumailov et al. · 0 citations
#machine learning Preprint Sep 2026

ALIBI: Adversarial Legitimacy Injection in Binary Input against LLM Malware Analyzers

ALIBI is presented, a semantic cover story attack against frontier LLM-based malware analyzers that adds a small, non-executed read-only section to a compiled binary, containing a coherent but false security product narrative, without altering imports or executable behavior.

H. Choi, Wonyoung Jung, Haehoon Seo et al. · 0 citations
Preprint Aug 2026

Validating LLM-Modernized Scientific Software Through Differential Fault Injection

A harness instruments the shared self-consistent-field driver of GAMESS at twelve sites and applies identical, deterministic faults to the original and LLM-modernized implementations, isolating the converted integral kernels.

Evan Coleman, Yuzhong Shen, M. Sosonkina et al. · 0 citations
#artificial intelligence Preprint Sep 2026

When LLM Decompilers Recompile More and Preserve Less

Decompile-Diverge is proposed, a behavioral comparison oracle not relying on fixed or hand-crafted tests: for each function it synthesizes a driver, grows a fuzzing corpus from the reference, and reruns the decompiled code on the same inputs to detect changes in the function's behavior.

Chang Liu, Edward Raff, Kristopher K. Micinski · 3 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.