arXiv subjects

Fabio Massacci

Publications and source records attributed to Fabio Massacci.

At least 19 recordsLinked to original sources

arXiv2026

CRSF: Collusion-Resilient Privacy-Preserving Sensor Fusion with Byzantine-Robust Participation

Privacy-preserving sensor fusion enables an untrusted server to compute an aggregate result over distributed sensor measurements without learning either individual inputs or the final output. Recent garbled-circuit-based protocols provide an efficient realization of this functionality in a sensor--server--client architecture, but remain vulnerable to sensor--server collusion and Byzantine manipulation of sensor participation. These weaknesses can compromise honest-sensor privacy, incorrectly exclude honest sensors, and corrupt the computed fusion result, thereby undermining the security guarantees expected from the protocol. We present CRSF, a collusion-resilient sensor-fusion protocol that addresses these weaknesses while providing privacy, correctness with explicit abort, and liveness. CRSF introduces a Practical Byzantine Fault Tolerance (PBFT)-based agreement phase for sensor submissions and uses server-specific, status-dependent label release with threshold protection of circuit-input labels. This design prevents any Byzantine server from unilaterally manipulating sensor participation and prevents any admissible sensor-server coalition from obtaining enough secret material to compromise honest-sensor privacy. We implement CRSF and compare its online execution time with the most relevant state-of-the-art baseline. Our Google Cloud evaluation measures the total computation and communication cost of the online protocol under fault-free and representative faulty executions. Across a range of fault-tolerant fusion circuits and up to 261 sensors, CRSF demonstrates a highly practical trade-off between robust security and protocol performance.

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arXiv2026

The Cathedral and the Bazaar of Software Vulnerabilities: From the NVD to the CNAs

For decades, the National Vulnerability Database (NVD), the "Cathedral", has been the reference source for vulnerability information for downstream research and industry tasks, e.g., software update prioritization. An emerging "Bazaar" of diverse CVE Numbering Authorities (CNAs) has created many alternative and sometimes diverging sources. We conduct a systematic analysis of divergence in Common Vulnerability Scoring System (CVSS) metrics covering the NVD and the public CNAs. We also check for self-divergence: two identical textual descriptions of CVEs with identical CWEs are rated differently by the same CNA. The odds of diverging are widespread, not uniform and sometimes unexpected. The assessment of Attack Complexity, User Interaction, and Impact are the major metrics where divergence happens. To understand the root causes, we perform a qualitative study by reaching out to the NVD and other CNAs (both open sources and proprietary products). We also discussed the findings at the CVSS Special Interest Group of FIRST, the community responsible for maintaining and evolving the CVSS standard. The key insights are that while something might be due to human errors, in some cases diverging is actually the right thing to do and might require changes in the way CVEs are generated industry-wide, in other cases explaining divergence requires access to additional FAQs. The good news is that the situation is improving since 2025, the bad news is that if one downloads the whole NVD (or another CNA dataset) from several years and uses it for predictions, the models trained on one source do not reliably generalize to a different source (accuracy can drop by 40%). We discuss the implications for practice and research.

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arXiv2026

Helpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Study

Software vulnerability remediation is a cognitively demanding task that requires specialized security expertise often lacking in general developers. In the meantime, Large Language Models (LLMs) assisted tools show potential in vulnerability detection, location, and repair tasks. [Hypothesis:] While LLM-assistance is hypothesized to accelerate patching, it also risks introducing hallucinations or insecure code, leading to a higher likelihood of generating superficial repairs that bypass the standard functionality checks but fail the security validation. [Objective:] We aim to present an empirical experiment, unveiling the capability of LLM-assisted vulnerability patching compared to manual debugging on human participants in real-world scenarios. [Method:] We plan to conduct a controlled experiment using a Balanced Crossover design. For that, we have developed a WebApp for code execution and integrated hidden Ghost Tests to verify patch integrity beyond visible functional requirements. The experiment involves training and evaluation scenarios. The remediation speed, remediation efficacy for both standard functionality tests and security tests, and participant perception will be evaluated. [Pilot Study:] A pilot experiment with a small sample of participants has been conducted, providing insights for the following study.

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arXiv2026

LLMs for Qualitative Data Analysis Fail on Security-specificComments in Human Experiments

[Background:] Thematic analysis of free-text justifications in human experiments provides significant qualitative insights. Yet, it is costly because reliable annotations require multiple domain experts. Large language models (LLMs) seem ideal candidates to replace human annotators. [Problem:] Coding security-specific aspects (code identifiers mentioned, lines-of-code mentioned, security keywords mentioned) may require deeper contextual understanding than sentiment classification. [Objective:] Explore whether LLMs can act as automated annotators for technical security comments by human subjects. [Method:] We prompt four top-performing LLMs on LiveBench to detect nine security-relevant codes in free-text comments by human subjects analyzing vulnerable code snippets. Outputs are compared to human annotators using Cohen's Kappa (chance-corrected accuracy). We test different prompts mimicking annotation best practices, including emerging codes, detailed codebooks with examples, and conflicting examples. [Negative Results:] We observed marked improvements only when using detailed code descriptions; however, these improvements are not uniform across codes and are insufficient to reliably replace a human annotator. [Limitations:] Additional studies with more LLMs and annotation tasks are needed.

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arXiv2026

On the Abolition of the "ICSE Paper" and the Adoption of the "Registered Proposal" and the "Results Report"

To address the 'novelty-vicious cycle' and the 'replicability crisis' of the field (both discussed in the survey) we propose abolishing the "ICSE paper" as we know it and replacing it with a two-tier system that also evolves the existing notion of 'Registered Report'. Authors proposing a new idea, experiment, or analysis would submit a "Registered Proposal" of their idea and the proposed experimental methodology to undergo peer review. The following year, anyone can submit (shorter) "Results Reports" on the realization of the empirical work based on the registered proposals of the previous ICSE (or FSE or ISSTA or ASE etc.). Both works should be first class citizens of the mainstream events. We argue that such a disruptive (heretical?) idea is supported and based on the responses of the community of the Future of Software Engineering pre-survey

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arXiv2026

Function Recovery Attacks in Gate-Hiding Garbled Circuits using SAT Solving

Semi-Private Function Evaluation (SPFE) enables joint computation while protecting both input data and the function itself. A practical instantiation is gate-hiding garbled circuits, which conceal gate functionalities while revealing circuit topology. Existing security definitions intentionally exclude leakage through topology, leaving its concrete impact on function privacy largely unexplored. We present a SAT-based function-recovery attack that reconstructs hidden gate operations from a circuit's public topology under two attacker knowledge models. Our approach combines topology-preserving simplification theorems with a decomposition of the recovery task into smaller SAT queries, thereby reducing the candidate gate-type assignment space and improving recovery performance. We evaluate the attack on ISCAS benchmarks, representative secure computation circuits, and fault-tolerant sensor fusion circuits under a 24-hour recovery budget. Compared to a baseline attack, the optimized version substantially reduces recovery time and, in some cases, completes recovery within the evaluation budget where the baseline does not. Our results show that revealing circuit topology can materially assist recovery of hidden gate functionality, identifying topology as a security-relevant leakage channel in gate-hiding garbled circuits.

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arXiv2025

Repairing vulnerabilities without invisible hands. A differentiated replication study on LLMs

Background: Automated Vulnerability Repair (AVR) is a fast-growing branch of program repair. Recent studies show that large language models (LLMs) outperform traditional techniques, extending their success beyond code generation and fault detection. Hypothesis: These gains may be driven by hidden factors -- "invisible hands" such as training-data leakage or perfect fault localization -- that let an LLM reproduce human-authored fixes for the same code. Objective: We replicate prior AVR studies under controlled conditions by deliberately adding errors to the reported vulnerability location in the prompt. If LLMs merely regurgitate memorized fixes, both small and large localization errors should yield the same number of correct patches, because any offset should divert the model from the original fix. Method: Our pipeline repairs vulnerabilities from the Vul4J and VJTrans benchmarks after shifting the fault location by n lines from the ground truth. A first LLM generates a patch, a second LLM reviews it, and we validate the result with regression and proof-of-vulnerability tests. Finally, we manually audit a sample of patches and estimate the error rate with the Agresti-Coull-Wilson method.

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arXiv2025

Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches

Vulnerability datasets used for ML testing implicitly contain retrospective information. When tested on the field, one can only use the labels available at the time of training and testing (e.g. seen and assumed negatives). As vulnerabilities are discovered across calendar time, labels change and past performance is not necessarily aligned with future performance. Past works only considered the slices of the whole history (e.g. DiverseVUl) or individual differences between releases (e.g. Jimenez et al. ESEC/FSE 2019). Such approaches are either too optimistic in training (e.g. the whole history) or too conservative (e.g. consecutive releases). We propose a method to restructure a dataset into a series of datasets in which both training and testing labels change to account for the knowledge available at the time. If the model is actually learning, it should improve its performance over time as more data becomes available and data becomes more stable, an effect that can be checked with the Mann-Kendall test. We validate our methodology for vulnerability detection with 4 time-based datasets (3 projects from BigVul dataset + Vuldeepecker's NVD) and 5 ML models (Code2Vec, CodeBERT, LineVul, ReGVD, and Vuldeepecker). In contrast to the intuitive expectation (more retrospective information, better performance), the trend results show that performance changes inconsistently across the years, showing that most models are not learning.

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arXiv2025

Using ML filters to help automated vulnerability repairs: when it helps and when it doesn't

[Context:] The acceptance of candidate patches in automated program repair has been typically based on testing oracles. Testing requires typically a costly process of building the application while ML models can be used to quickly classify patches, thus allowing more candidate patches to be generated in a positive feedback loop. [Problem:] If the model predictions are unreliable (as in vulnerability detection) they can hardly replace the more reliable oracles based on testing. [New Idea:] We propose to use an ML model as a preliminary filter of candidate patches which is put in front of a traditional filter based on testing. [Preliminary Results:] We identify some theoretical bounds on the precision and recall of the ML algorithm that makes such operation meaningful in practice. With these bounds and the results published in the literature, we calculate how fast some of state-of-the art vulnerability detectors must be to be more effective over a traditional AVR pipeline such as APR4Vuln based just on testing.

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arXiv2025

Large Language Models Are Unreliable for Cyber Threat Intelligence

Several recent works have argued that Large Language Models (LLMs) can be used to tame the data deluge in the cybersecurity field, by improving the automation of Cyber Threat Intelligence (CTI) tasks. This work presents an evaluation methodology that other than allowing to test LLMs on CTI tasks when using zero-shot learning, few-shot learning and fine-tuning, also allows to quantify their consistency and their confidence level. We run experiments with three state-of-the-art LLMs and a dataset of 350 threat intelligence reports and present new evidence of potential security risks in relying on LLMs for CTI. We show how LLMs cannot guarantee sufficient performance on real-size reports while also being inconsistent and overconfident. Few-shot learning and fine-tuning only partially improve the results, thus posing doubts about the possibility of using LLMs for CTI scenarios, where labelled datasets are lacking and where confidence is a fundamental factor.

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arXiv2025

Optimisation of cyber insurance coverage with selection of cost effective security controls

Nowadays, cyber threats are considered among the most dangerous risks by top management of enterprises. One way to deal with these risks is to insure them, but cyber insurance is still quite expensive. The insurance fee can be reduced if organisations improve their cyber security protection, i.e., reducing the insured risk. In other words, organisations need an investment strategy to decide the optimal amount of investments into cyber insurance and self-protection. In this work, we propose an approach to help a risk-averse organisation to distribute its cyber security investments in a cost-efficient way. What makes our approach unique is that next to defining the amount of investments in cyber insurance and self-protection, our proposal also explicitly defines how these investments should be spent by selecting the most cost-efficient security controls. Moreover, we provide an exact algorithm for the control selection problem considering several threats at the same time and compare this algorithm with other approximate algorithmic solutions.

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arXiv2025

A Systematic Literature Review on Automated Exploit and Security Test Generation

The exploit or the Proof of Concept of the vulnerability plays an important role in developing superior vulnerability repair techniques, as it can be used as an oracle to verify the correctness of the patches generated by the tools. However, the vulnerability exploits are often unavailable and require time and expert knowledge to craft. Obtaining them from the exploit generation techniques is another potential solution. The goal of this survey is to aid the researchers and practitioners in understanding the existing techniques for exploit generation through the analysis of their characteristics and their usability in practice. We identify a list of exploit generation techniques from literature and group them into four categories: automated exploit generation, security testing, fuzzing, and other techniques. Most of the techniques focus on the memory-based vulnerabilities in C/C++ programs and web-based injection vulnerabilities in PHP and Java applications. We found only a few studies that publicly provided usable tools associated with their techniques.

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arXiv2024

Forecasting the risk of software choices: A model to foretell security vulnerabilities from library dependencies and source code evolution

Software security mainly studies vulnerability detection: is my code vulnerable today? This hinders risk estimation, so new approaches are emerging to forecast the occurrence of future vulnerabilities. While useful, these approaches are coarse-grained and hard to employ for project-specific technical decisions. We introduce a model capable of vulnerability forecasting at library level. Formalising source-code evolution in time together with library dependency, our model can estimate the probability that a software project faces a CVE disclosure in a future time window. Our approach is white-box and lightweight, which we demonstrate via experiments involving 1255 CVEs and 768 Java libraries, made public as an open-source artifact. Besides probabilities estimation, e.g. to plan software updates, this formal model can be used to detect security-sensitive points in a project, or measure the health of a development ecosystem.

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arXiv2024

How hard can it be? Quantifying MITRE attack campaigns with attack trees and cATM logic

The landscape of cyber threats grows more complex by the day. Advanced Persistent Threats carry out attack campaigns - e.g. operations Dream Job, Wocao, and WannaCry - against which cybersecurity practitioners must defend. To prioritise which of these to defend against, cybersecurity experts must be equipped with the right toolbox to evaluate the most threatening ones. In particular, they would strongly benefit from (a) an estimation of the likelihood values for each attack recorded in the wild, and (b) transparently operationalising these values to compare campaigns quantitatively. Security experts could then perform transparent and accountable quantitatively-informed decisions. Here we construct such a framework: (1) quantifying the likelihood of attack campaigns via data-driven procedures on the MITRE knowledge-base, (2) introducing a methodology for automatic modelling of MITRE intelligence data, that captures any attack campaign via template attack tree models, and (3) proposing an open-source tool to perform these comparisons based on the cATM logic. Finally, we quantify the likelihood of all MITRE Enterprise campaigns, and compare the likelihood of the Wocao and Dream Job MITRE campaigns - generated with our proposed approach - against manually-built attack tree models. We demonstrate how our methodology is substantially lighter in modelling effort, and capable of capturing all the quantitative relevant data.

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arXiv2024

Risks of ignoring uncertainty propagation in AI-augmented security pipelines

The use of AI technologies is being integrated into the secure development of software-based systems, with an increasing trend of composing AI-based subsystems (with uncertain levels of performance) into automated pipelines. This presents a fundamental research challenge and seriously threatens safety-critical domains. Despite the existing knowledge about uncertainty in risk analysis, no previous work has estimated the uncertainty of AI-augmented systems given the propagation of errors in the pipeline. We provide the formal underpinnings for capturing uncertainty propagation, develop a simulator to quantify uncertainty, and evaluate the simulation of propagating errors with one case study. We discuss the generalizability of our approach and its limitations and present recommendations for evaluation policies concerning AI systems. Future work includes extending the approach by relaxing the remaining assumptions and by experimenting with a real system.

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arXiv2024

Using AI Assistants in Software Development: A Qualitative Study on Security Practices and Concerns

Following the recent release of AI assistants, such as OpenAI's ChatGPT and GitHub Copilot, the software industry quickly utilized these tools for software development tasks, e.g., generating code or consulting AI for advice. While recent research has demonstrated that AI-generated code can contain security issues, how software professionals balance AI assistant usage and security remains unclear. This paper investigates how software professionals use AI assistants in secure software development, what security implications and considerations arise, and what impact they foresee on secure software development. We conducted 27 semi-structured interviews with software professionals, including software engineers, team leads, and security testers. We also reviewed 190 relevant Reddit posts and comments to gain insights into the current discourse surrounding AI assistants for software development. Our analysis of the interviews and Reddit posts finds that despite many security and quality concerns, participants widely use AI assistants for security-critical tasks, e.g., code generation, threat modeling, and vulnerability detection. Their overall mistrust leads to checking AI suggestions in similar ways to human code, although they expect improvements and, therefore, a heavier use for security tasks in the future. We conclude with recommendations for software professionals to critically check AI suggestions, AI creators to improve suggestion security and capabilities for ethical security tasks, and academic researchers to consider general-purpose AI in software development.

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arXiv2024

Analyzing and Mitigating (with LLMs) the Security Misconfigurations of Helm Charts from Artifact Hub

Background: Helm is a package manager that allows defining, installing, and upgrading applications with Kubernetes (K8s), a popular container orchestration platform. A Helm chart is a collection of files describing all dependencies, resources, and parameters required for deploying an application within a K8s cluster. Objective: The goal of this study is to mine and empirically evaluate the security of Helm charts, comparing the performance of existing tools in terms of misconfigurations reported by policies available by default, and measure to what extent LLMs could be used for removing misconfiguration. We also want to investigate whether there are false positives in both the LLM refactorings and the tool outputs. Method: We propose a pipeline to mine Helm charts from Artifact Hub, a popular centralized repository, and analyze them using state-of-the-art open-source tools, such as Checkov and KICS. First, such a pipeline will run several chart analyzers and identify the common and unique misconfigurations reported by each tool. Secondly, it will use LLMs to suggest mitigation for each misconfiguration. Finally, the chart refactoring previously generated will be analyzed again by the same tools to see whether it satisfies the tool's policies. At the same time, we will also perform a manual analysis on a subset of charts to evaluate whether there are false positive misconfigurations from the tool's reporting and in the LLM refactoring.

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arXiv2024

Cross-ecosystem categorization: A manual-curation protocol for the categorization of Java Maven libraries along Python PyPI Topics

Context: Software of different functional categories, such as text processing vs. networking, has different profiles in terms of metrics like security and updates. Using popularity to compare e.g. Java vs. Python libraries might give a skewed perspective, as the categories of the most popular software vary from one ecosystem to the next. How can one compare libraries datasets across software ecosystems, when not even the category names are uniform among them? Objective: We study how to generate a language-agnostic categorisation of software by functional purpose, that enables cross-ecosystem studies of libraries datasets. This provides the functional fingerprint information needed for software metrics comparisons. Method: We designed and implemented a human-guided protocol to categorise libraries from software ecosystems. Category names mirror PyPI Topic classifiers, but the protocol is generic and can be applied to any ecosystem. We demonstrate it by categorising 256 Java/Maven libraries with severe security vulnerabilities. Results: The protocol allows three or more people to categorise any number of libraries. The categorisation produced is functional-oriented and language-agnostic. The Java/Maven dataset demonstration resulted in a majority of Internet-oriented libraries, coherent with its selection by severe vulnerabilities. To allow replication and updates, we make the dataset and the protocol individual steps available as open data. Conclusions: Libraries categorisation by functional purpose is feasible with our protocol, which produced the fingerprint of a 256-libraries Java dataset. While this was labour intensive, humans excel in the required inference tasks, so full automation of the process is not envisioned. However, results can provide the ground truth needed for machine learning in large-scale cross-ecosystem empirical studies.

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