Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.21146

ExecWeave: Cross-Layer Execution Provenance for Observable Agentic Software

Guo-Wei Wong·Meng Chang Chen
View original source ↗Read source document ↗Export metadata ↓

Abstract

Agentic software invokes tools, launches processes, accesses files, and communicates with local or remote services. Application traces describe logical operations, whereas operating-system telemetry records machine activity without necessarily identifying the responsible agent action. Relating these evidence planes is a reconstruction problem: a plausible but unsupported association can assign an effect to the wrong principal. We present ExecWeave, a local-first system that materializes provider semantics and independently observed runtime events into a typed execution graph. The implementation retains separate source artifacts, distinguishes logical occurrences from repeated evidence, and records attribution, inference, and causal-status metadata. An optional command-to-process correlator emits a non-causal inferred bridge only when its bounded search yields one compatible observed process; uniqueness within this observed candidate set is not a guarantee of causal correctness. OS-observed file and network effects acquire a semantic tool or agent principal only through an observed process plus a justified command-to-process association; session-correlated file-system events do not by themselves provide that principal. Source code is available at https://github.com/Irish-kw/ExecWeave.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Guo-Wei Wong, Meng Chang Chen. 2026-10-15. ExecWeave: Cross-Layer Execution Provenance for Observable Agentic Software. https://arxiv.org/abs/2610.21146

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

An Empirical Analysis of EOS Blockchain: Architecture, Contract, and Security

With the rapid development of blockchain technology, various blockchain systems are exhibiting vitality and potential. As a representative of Blockchain 3.0, the EOS blockchain has been regarded as a strong competitor to Ethereum. Nevertheless, compared with Bitcoin and Ethereum, academic research and in-depth analyses of EOS remain scarce. To address this gap, this study conducts a comprehensive investigation of the EOS blockchain from five key dimensions: system architecture, decentralization, performance, smart contracts, and behavioral security. The architectural analysis focuses on six core components of the EOS system, detailing their functionalities and operational workflows. The decentralization and performance evaluations, based on data from the XBlock data-sharing platform, reveal several critical issues: low account activity, limited participation in the supernode election process, minimal variation in the set of block producers, and a substantial gap between actual throughput and the claimed million-level performance. Five types of contract vulnerabilities are identified in the smart contract dimension, and four mainstream vulnerability detection platforms are introduced and comparatively analyzed. In terms of behavioral security, four real-world attacks targeting the structural characteristics of EOS are summarized. This study contributes to the ongoing development of the EOS blockchain and provides valuable insights for enhancing the security and regulatory mechanisms of blockchain ecosystems.

cs.CR↗
arXiv2026

ADCA: Attention-Driven Multi-Party Collusion Attack in Federated Self-Supervised Learning

Federated Self-Supervised Learning (FSSL) integrates the privacy advantages of distributed training with the capability of self-supervised learning to leverage unlabeled data, showing strong potential across applications. However, recent studies have shown that FSSL is also vulnerable to backdoor attacks. Existing attacks are limited by their trigger design, which typically employs a global, uniform trigger that is easily detected, gets diluted during aggregation, and lacks robustness in heterogeneous client environments. To address these challenges, we propose the Attention-Driven multi-party Collusion Attack (ADCA). During local pre-training, malicious clients decompose the global trigger to find optimal local patterns. Subsequently, these malicious clients collude to form a malicious coalition and establish a collaborative optimization mechanism within it. In this mechanism, each submits its model updates, and an attention mechanism dynamically aggregates them to explore the best cooperative strategy. The resulting aggregated parameters serve as the initial state for the next round of training within the coalition, thereby effectively mitigating the dilution of backdoor information by benign updates. Experiments on multiple FSSL scenarios and four datasets show that ADCA significantly outperforms existing methods in Attack Success Rate (ASR) and persistence, proving its effectiveness and robustness.

cs.CR↗
arXiv2026

Identifier-Free Code Embedding Models for Scalable Search

Function association is a useful process for binary reverse engineers. Search tools exist to perform association at scale, but they do not utilize the full range of capabilities that AI-enabled search provides. Prior work has explored the development of embedding models for association between certain reverse engineering code representations, but that work does not cover bidirectional association between source code and decompiled, stripped code with standard preprocessing requirements. To bridge this gap, we formalize this function association problem and evaluate the extent to which embedding models can bidirectionally associate between these two representations. To improve model performance at this task, we fine-tune a Qwen3-Embedding model with contrastive learning. We find that our new model outperforms other models on all function association baselines by a substantial margin and generalizes to a constant-algorithm association task it is not explicitly trained on.

cs.CR↗