Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2606.13037

DIG: Oracle-Guided Directed Input Generation for One-Day Vulnerabilities

Andrew Bao·Haochen Zeng·Peng Chen·Stephen McCamant·Pen-Chung Yew
View original source ↗Read source document ↗Export metadata ↓

Abstract

One-day vulnerabilities pose significant risks due to delayed or incomplete patch adoption. Generating proof-of-concept (PoC) inputs is therefore essential for assessing real-world impact. The key challenge is identifying necessary constraints for triggering the vulnerability and solving them effectively. Existing directed fuzzing approaches prioritize inputs toward target locations, but neither explicitly identify necessary constraints nor solve them effectively, relying instead on target-distance feedback and random mutation. Agentic approaches show strong potential through code reasoning and structured input generation, but goal drift in long-horizon reasoning limits their effectiveness. DIG addresses this challenge by exploiting a key property of one-day vulnerabilities: patches often reveal necessary preconditions for triggering. DIG uses an LLM to analyze the patch and synthesize an oracle making these conditions explicit. The oracle supports effective PoC generation at two levels. At the high level, DIG performs oracle-guided generator evolution, where an agent infers and solves constraints to satisfy the oracle. At the low level, DIG instruments the oracle into the target program and uses branch-distance feedback to guide random mutation in directed fuzzing. Evaluation shows DIG outperforms 2 state-of-the-art agents and 10 fuzzers across 138 real-world CVEs. DIG triggers 80 vulnerabilities, surpassing prior results and outperforming the best baseline by 40% (57 vs. 80 CVEs). Notably, DIG exclusively triggers 9 vulnerabilities no existing technique can trigger. Compared to the average of other tools, DIG triggers vulnerabilities faster in 92.9% of cases, achieving over 100x speedup in 48.8% of cases, with a maximum speedup of 3,664x. Beyond one-day PoC generation, DIG uncovers 6 previously unknown vulnerabilities in widely deployed libraries, enabling zero-day discovery.

Explore related subjects

cs.CRcs.SE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Andrew Bao, Haochen Zeng, Peng Chen, Stephen McCamant, Pen-Chung Yew. 2026-06-11. DIG: Oracle-Guided Directed Input Generation for One-Day Vulnerabilities. https://arxiv.org/abs/2606.13037

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

KEEP EXPLORING

Related papers

arXiv2026

Starfish: Rebalancing Multi-Party Off-Chain Payment Channels

Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network (PCN), as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channel, restricting the ability of PCNs to transact through a path and subsequently reducing the transaction success rate. To address this issue, the technology of rebalancing has been proposed. However, existing rebalancing strategies in PCNs are limited in their efficiency and applicability under realistic network topologies. Cycle-based approaches only enable rebalancing among nodes that form cycles, while non-cycle-based approaches incur high on-chain operation costs. In this study, we propose Starfish, a rebalancing approach designed for the locally star-like structures formed by high-degree routing hubs and their incident channels in real-world PCNs. By leveraging a hub-centric structure, Starfish reduces the on-chain overhead of rebalancing from quadratic to linear in the number of channels, achieving high rebalancing efficiency. We formally prove the security of Starfish and conduct comparative experiments against existing rebalancing techniques to demonstrate its effectiveness.

cs.CR↗
arXiv2026

Proof-of-Authorship for Diffusion-based AI Generated Content

Recent advancements in AI-generated content (AIGC) have introduced new challenges in intellectual property protection and the authentication of generated objects. We focus on scenarios in which an author seeks to assert authorship of an object generated using latent diffusion models (LDMs), in the presence of adversaries who attempt to falsely claim authorship of objects they did not create. While proof-of-ownership has been studied in the context of multimedia content through techniques such as time-stamping and watermarking, these approaches face notable limitations. In contrast to traditional content creation sources (e.g., cameras), the LDM generation process offers greater control to the author. Specifically, the random seed used during generation can be deliberately chosen. By binding the seed to the author's identity using cryptographic functions, the author can assert to be the creator of the object. We refer to this stronger guarantee as proof-of-authorship, since only the creator of the object can legitimately claim the object. This contrasts with proof-of-ownership via time-stamping or watermarking, where any entity could potentially claim ownership of an object by being the first to timestamp or embed the watermark. We propose a proof-of-authorship framework involving a probabilistic adjudicator who quantifies the probability that a claim is false. Furthermore, unlike prior approaches, the proposed framework does not involve any secret. We explore various attack scenarios and analyze design choices using Stable Diffusion 2.1 and XL as representative case studies.

cs.CR↗
arXiv2026

Impact Analysis of Speech Representation Learning Models for Acoustic Side-Channel Attack

Acoustic side-channel attacks (ASCA) on keyboards have gained increasing attention, yet impact of speech representation learning models in ASCA remains unexplored. Addressing this, we introduce KEYAC, a dataset designed to analyze representation generalization for ASCA under both standard and VoIP codec settings. On KEYAC, we evaluate six representation learning models under zero-shot and partial fine-tuning settings using fully connected and convolutional networks. Results show that while partial fine-tuning improves performance, models struggle to generalize across VoIP codecs. We hypothesize this limitation stems from inadequate modeling of nonlinear feature interactions in conventional fine-tuning architectures. To address this, we employ Kolmogorov-Arnold Networks (KAN) for fine-tuning. Empirical results show that KAN-based fine-tuning consistently outperforms the baselines and establishes a new state-of-the-art on KEYAC.

cs.CR↗