With the widespread use of the Internet of Things (IoT) and mobile devices, binary programs are widely used in human life. From smart homes and car systems to industrial control systems, underlying firmware and native executable files form the core of these critical infrastructures. In traditional vulnerability discovery, researchers need to manually analyze function relationships and function content. With the rapid development of Artificial Intelligence (AI), large language models (LLMs) are utilized for analysis, replacing manual analysis. However, sending fully decompiled functions to LLMs consumes a large number of tokens, resulting in high costs and slow responses, particularly for large binaries. Many existing methods treat all functions equally, without focusing on those more likely to be vulnerable. In this paper, we proposed a token-efficient binary vulnerability prioritization framework. Our framework allows the LLM to analyze function names to identify potentially risky functions, and then enables the LLM to examine only these selected functions in detail. What’s more, we introduce a metric called token-detection efficiency (TDE) to better demonstrate the efficiency of our proposed framework. The experiment shows that our framework achieves higher vulnerability detection accuracy with fewer tokens.
A common approach to protecting data confidentiality again-st potential adversaries is to encrypt the databases. Nevertheless, it is infeasible to perform meaningful computations on a ciphertext. Trusted Execution Environments (TEEs) offer a promising alternative by enabling computations on plaintext within a secure enclave, thereby supporting rich and complex queries. However, existing works either fall short in providing strong oblivious guarantees, fail to support queries such as non-equi joins, or suffer from significant efficiency bottlenecks. In this paper, we first address the oblivious order counting and encoding problem in a parallelized manner, enabling the counting and encoding of elements that satisfy a given order within an unordered sequence. Building on this, we propose parallelizable non-equi join algorithms for both single condition and multiple conditions. The experimental results show that our scheme completes a non-equi-join with a single condition, producing 16,000,000 output records in 26.4 s, and a multiple conditions non-equi-join producing 26,366,724 output records in 19.4 s, achieving up to 4.7 × and 2.04 × speedups over the state-of-the-art scheme, respectively.
The CKKS scheme enables approximate arithmetic over encrypted vectors, supporting SIMD computation and controlled ciphertext rotations without revealing plaintexts. Prior encrypted fuzzy-query systems for English text typically map each character to a small, contiguous code and evaluate equality or similarity with low-degree polynomials. This design does not transfer to Chinese: the Unicode space is large and discontinuous, and after normalization for CKKS distinct characters can become numerically proximate, leading to collisions under approximation noise and undermining both equality tests and fuzzy comparisons. We address these limitations with two encoding strategies Digit Decomposition Embedding (DDE) and Bit Slicing (BS) that represent each Chinese character by several low-magnitude integer components, preserving inter-character separability at low multiplicative depth and remaining compatible with non-bootstrapping CKKS. We further introduce a slot-masking and sliding-window mechanism that leverages structured slot alignment and bounded rotations to realize fuzzy matching and substring retrieval. Together, these components provide a structured Chinese encoding framework for multi-field encrypted fuzzy query that maintains numeric distinguishability, controls depth growth, and exploits CKKS batch parallelism for efficient end-to-end execution.
Recently, growing public concern over data privacy has led to the emergence of the ‘right to be forgotten’ under the European Union’s General Data Protection Regulation (GDPR), which allows individuals to request the deletion of personal data. In response, researchers have developed methods to selectively remove or mitigate the influence of specific data on machine learning models, a process known as machine unlearning (MUL). However, while most research on MUL has focused exclusively on maintaining model accuracy after unlearning, the robustness of these models has been largely overlooked, especially under adversarial attacks. In this study, we examine whether unlearning degrades robustness in the visual classification task by performing adversarial attacks on various unlearned methods and compare performance against a retrained-from-scratch baseline, including evaluations using a standard corruption dataset. Our comprehensive evaluation across seven unlearning methods reveals consistent degradation in robustness under perturbation, supporting our hypothesis that machine unlearning degrades adversarial robustness compared to retraining from scratch, and highlighting the need for methods that preserve robustness without compromising the effectiveness and efficiency.
The proliferation of open-source software (OSS) has made software supply chains prime targets for attacks like Package Confusion, where adversaries publish malicious packages with names deceptively similar to legitimate ones. Existing detection methods often rely on simple lexical similarity or passive analysis of known package pairs, struggle with high false positive rates (FPR), fail to proactively identify emerging threats, and are vulnerable to adversarial evasion. To overcome these limitations, we introduce AgentGuard, a novel framework for proactive, single-input package confusion detection. AgentGuard employs a multi-agent architecture that autonomously discovers potential confusion targets using fine-tuned word embedding model to hybird semantic search and subsequently evaluates the risk via a machine learning model incorporating multi-dimensional feature groups to enhance robustness. This design enables scalable, real-time monitoring across diverse software ecosystems. We evaluate AgentGuard on the challenging ConfuDB and NeupaneDB datasets. Our results demonstrate that AgentGuard significantly outperforms state-of-the-art baselines, improving accuracy by 10
