Protocol implementations are stateful which makes them difficult to test: Sending the same test input message twice might yield a different response every time. Our proposal to consider a sequence of messages as a seed for coverage-directed greybox fuzzing, to associate each message with the corresponding protocol state, and to maximize the coverage of both the state space and the code was first published in 2020 in a short tool demonstration paper. AFLNet was the first code- and state-coverage-guided protocol fuzzer; it used the response code as an indicator of the current protocol state. Over the past five years, the tool paper has gathered hundreds of citations, the code repository was forked almost 200 times and has seen over thirty pull requests from practitioners and researchers, and our initial proposal has been improved upon in many significant ways. In this paper, we first provide an extended discussion and a full empirical evaluation of the technical contributions of AFLNet and then reflect on the impact that our approach and our tool had in the past five years, on both the research and the practice of protocol fuzzing.

Misinformation remains one of the most significant issues in the digital age. While automated fact-checking has emerged as a viable solution, most current systems are limited to evaluating factual accuracy. However, the detrimental effect of misinformation transcends simple falsehoods; it takes advantage of how individuals perceive, interpret, and emotionally react to information. This underscores the need to move beyond factuality and adopt more human-centered detection frameworks. In this survey, we explore the evolving interplay between traditional fact-checking approaches and psychological concepts such as cognitive biases, social dynamics, and emotional responses. By analyzing state-of-the-art misinformation detection systems through the lens of human psychology and behavior, we reveal critical limitations of current methods and identify opportunities for improvement. Additionally, we outline future research directions aimed at creating more robust and adaptive frameworks, such as neuro-behavioural models that integrate technological factors with the complexities of human cognition and social influence. These approaches offer promising pathways to more effectively detect and mitigate the societal harms of misinformation.

Generating code from natural language specifications is one of the most successful applications of Large Language Models (LLMs). Yet, they hallucinate: LLMs produce outputs that may be grammatically correct but are factually incorrect. Without an existing, correct implementation (i.e., an oracle), can we quantify how likely the generated program is correct? In this paper, we propose a measure of incorrectness, called incoherence, that can be estimated efficiently in the absence of an oracle and provides a lower bound on the error, i.e., the probability that the LLM-generated program for that specification is incorrect. Our experiments demonstrate an extraordinary effectiveness. For the average code generation task, our incoherence-based methodology can automatically identify about two-thirds of incorrect programs without reports of false positives. In fact, an oracle-based evaluation of LLMs can be reliably replaced by an incoherence-based evaluation. In particular, we find a very strong agreement between the ranking of LLMs by the number of programs deemed correct via an oracle (pass@1) and the ranking of LLMs by the number of programs deemed correct via our incoherence.

One of the biggest attack surfaces of embedded systems is their network interfaces, which enable communication with other devices. Unlike their general-purpose counterparts, embedded systems are designed for specialized use cases, resulting in unique and diverse communication stacks. Unfortunately, current approaches for evaluating the security of these embedded network stacks require manual effort or access to hardware, and they generally focus only on small parts of the embedded system. A promising alternative is firmware rehosting, which enables fuzz testing of the entire firmware by generically emulating the physical hardware. However, existing rehosting methods often struggle to meaningfully explore network stacks due to their complex, multi-layered input formats. This limits their ability to uncover deeply nested software faults. To address this problem, we introduce a novel method to automatically detect and handle the use of network protocols in firmware called Pemu. By automatically deducing the available network protocols, Pemu can transparently generate valid network packets that encapsulate fuzzing data, allowing the fuzzing input to flow directly into deeper layers of the firmware logic. Our approach thus enables a deeper, more targeted, and layer-by-layer analysis of firmware components that were previously difficult or impossible to test. Our evaluation demonstrates that Pemu consistently improves the code coverage of three existing rehosting tools for embedded network stacks. Furthermore, our fuzzer rediscovered several known vulnerabilities and identified five previously unknown software faults, highlighting its effectiveness in uncovering deeply nested bugs in network-exposed code.

Public opinion manipulation has entered a new phase, amplifying its roots in rhetoric and propaganda. Advances in large language models (LLMs) and autonomous agents now let influence campaigns reach unprecedented scale and precision. Researchers warn AI could foster mass manipulation. Generative tools can expand propaganda output without sacrificing credibility and inexpensively create election falsehoods that are rated as more human-like than those written by humans. Techniques meant to refine AI reasoning, such as chain-of-thought prompting, can just as effectively be used to generate more convincing falsehoods. Enabled by these capabilities, another disruptive threat is emerging: swarms of collaborative, malicious AI agents. Fusing LLM reasoning with multi-agent architectures, these systems are capable of coordinating autonomously, infiltrating communities, and fabricating consensus cheaply. By adaptively mimicking human social dynamics, they threaten democracy.

Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of, algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders.

Quantum key distribution (QKD) enables Alice and Bob to exchange a secret key over a public, untrusted quantum channel. Compared to classical key exchange, QKD achieves everlasting security: after the protocol execution the key is secure against adversaries that can do unbounded computations. On the flip side, while classical key exchange can be achieved non-interactively (with two simultaneous messages between Alice and Bob), no non-interactive protocol is known that provides everlasting security, even using quantum information. In this work, we make progress on this problem. Our main technical contribution is a computational variant of the celebrated monogamy of entanglement game, where the secret is only computationally hidden from the players, rather than information-theoretically. In these settings, we prove a negligible bound on the maximal winning probability over all strategies. As a direct application, we obtain a non-interactive (simultaneous message) QKD protocol from any post-quantum classical non-interactive key exchange, which satisfies everlastingly secure assuming Alice and Bob agree on the same key. The protocol only uses EPR pairs and standard and Hadamard basis measurements, making it suitable for near-term quantum hardware. We also propose how to convert this protocol into a two-round protocol that satisfies the standard notion of everlasting security. Finally, we prove a no-go theorem which establishes that (in contrast to the case of ordinary multi-round QKD) entanglement is necessary for non-interactive QKD, i.e., the messages sent by Alice and Bob cannot both be unentangled with their respective quantum memories if the protocol is to be everlastingly secure.

How to find memory safety bugs efficiently when navigating a symbolic execution tree that suffers from path explosion? Existing solutions either adopt path search heuristics to maximize coverage rate or chopped symbolic execution to skip uninteresting code (i.e., manually labeled as vulnerability-unrelated) during path exploration. However, most existing search heuristics are not vulnerability-oriented, and manual labeling of irrelevant code-to-be-skipped relies heavily on prior expert knowledge, making it hard to detect vulnerabilities effectively in practice. This paper proposes Vital, a new vulnerability-oriented symbolic execution via type-unsafe pointer-guided Monte Carlo Tree Search (MCTS). A pointer that is type unsafe cannot be statically proven to be safely dereferenced without memory corruption. Our key hypothesis is that a path with more type unsafe pointers is more likely to contain vulnerabilities. Vital drives a guided MCTS to prioritize paths in the symbolic execution tree that contain a larger number of unsafe pointers and to effectively navigate the exploration-exploitation trade-off. We built Vital on top of KLEE and compared it with existing search strategies and chopped symbolic execution. In the former, the results demonstrate that Vital could cover up to 90.03% more unsafe pointers and detect up to 37.50% more unique memory errors. In the latter, the results show that Vital could achieve a speedup of up to 30x execution time and a reduction of up to 20x memory consumption on automatically detecting known vulnerabilities without prior expert knowledge.

Access to Water, Sanitation, and Hygiene (WASH) services remains a major public health concern in refugee camps. This study introduces a remote sensing-driven framework to quantify WASH accessibility-specifically to water pumps, latrines, and bathing cubicles-in the Rohingya camps of Cox's Bazar, one of the world's most densely populated displacement settings. Detecting refugee shelters in such emergent camps presents substantial challenges, primarily due to their dense spatial configuration and irregular geometric patterns. Using sub-meter satellite images, we develop a semi-supervised segmentation framework that achieves an F1-score of 76.4% in detecting individual refugee shelters. Applying the framework across multi-year data reveals declining WASH accessibility, driven by rapid refugee population growth and reduced facility availability, rising from 25 people per facility in 2022 to 29.4 in 2025. Gender-disaggregated analysis further shows that women and girls experience reduced accessibility, in scenarios with inadequate safety-related segregation in WASH facilities. These findings suggest the importance of demand-responsive allocation strategies that can identify areas with under-served populations-such as women and girls-and ensure that limited infrastructure serves the greatest number of people in settings with fixed or shrinking budgets. We also discuss the value of high-resolution remote sensing and machine learning to detect inequality and inform equitable resource planning in complex humanitarian environments.

Even today, over 70% of security vulnerabilities in critical software systems result from memory safety violations. To address this challenge, fuzzing and static analysis are widely used automated methods to discover such vulnerabilities. Fuzzing generates random program inputs to identify faults, while static analysis examines source code to detect potential vulnerabilities. Although these techniques share a common goal, they take fundamentally different approaches and have evolved largely independently. In this paper, we present an empirical analysis of five static analyzers and 13 fuzzers, applied to over 100 known security vulnerabilities in C/C++ programs. We measure the number of bug reports generated for each vulnerability to evaluate how the approaches differ and complement each other. Moreover, we randomly sample eight bug-containing functions, manually analyze all bug reports therein, and quantify false-positive rates. We also assess limits to bug discovery, ease of use, resource requirements, and integration into the development process. We find that both techniques discover different types of bugs, but there are clear winners for each. Developers should consider these tools depending on their specific workflow and usability requirements. Based on our findings, we propose future directions to foster collaboration between these research domains.

Intellectual Property (IP) theft is a cause of major financial and reputational damage, reportedly in the range of hundreds of billions of dollars annually in the U.S. alone. Field Programmable Gate Arrays (FPGAs) are particularly exposed to IP theft, because their configuration file contains the IP in a proprietary format that can be mapped to a gate-level netlist with moderate effort. Despite this threat, the scientific understanding of this issue lacks behind reality, thereby preventing an in-depth assessment of IP theft from FPGAs in academia. We address this discrepancy through a real-world case study on a Lattice iCE40 FPGA found inside iPhone 7. Apple refers to this FPGA as Maggie. By reverse engineering the proprietary signal-processing algorithm implemented on Maggie, we generate novel insights into the actual efforts required to commit FPGA IP theft and the challenges an attacker faces on the way. Informed by our case study, we then introduce generalized netlist reverse engineering techniques that drastically reduce the required manual effort and are applicable across a diverse spectrum of FPGA implementations and architectures. We evaluate these techniques on six benchmarks that are representative of different FPGA applications and have been synthesized for Xilinx and Lattice FPGAs, as well as in an end-to-end white-box case study. Finally, we provide a comprehensive open-source tool suite of netlist reverse engineering techniques to foster future research, enable the community to perform realistic threat assessments, and facilitate the evaluation of novel countermeasures.

Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure prediction, has been widely adopted by researchers. The availability of easy-to-use interfaces and interpretable outcomes from the neural network architecture, such as the confidence scores used to color the predicted structures, have made AlphaFold accessible even to non-ML experts. In this paper, we present various methods for representing protein 3D structures from low- to high-resolution, and show how interpretable ML methods can support tasks such as predicting protein structures, protein function, and protein-protein interactions. This survey also emphasizes the significance of interpreting and visualizing ML-based inference for structure-based protein representations that enhance interpretability and knowledge discovery. Developing such interpretable approaches promises to further accelerate fields including drug development and protein design.

Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval. As a result, there is increasing social and legal pressure to provide explanations that help the affected individuals not only to understand why a prediction was output, but also how to act to obtain a desired outcome. To this end, several works have proposed optimization-based methods to generate nearest counterfactual explanations. However, these methods are often restricted to a particular subset of models (e.g., decision trees or linear models) and differentiable distance functions. In contrast, we build on standard theory and tools from formal verification and propose a novel algorithm that solves a sequence of satisfiability problems, where both the distance function (objective) and predictive model (constraints) are represented as logic formulae. As shown by our experiments on real-world data, our algorithm is: i) model-agnostic ({non-}linear, {non-}differentiable, {non-}convex); ii) data-type-agnostic (heterogeneous features); iii) distance-agnostic ($\ell_0, \ell_1, \ell_\infty$, and combinations thereof); iv) able to generate plausible and diverse counterfactuals for any sample (i.e., 100% coverage); and v) at provably optimal distances.

This paper investigates the digital security experiences of four at-risk user groups in Germany, including older adults (70+), teenagers (14-17), people with migration backgrounds, and people with low formal education. Using computer-assisted telephone interviews, we sampled 250 participants per group, representative of region, gender, and partly age distributions. We examine their device usage, concerns, prior negative incidents, perceptions of potential attackers, and information sources. Our study provides the first quantitative and nationally representative insights into the digital security experiences of these four at-risk groups in Germany. Our findings show that participants with migration backgrounds used the most devices, sought more security information, and reported more experiences with cybercrime incidents than other groups. Older adults used the fewest devices and were least affected by cybercrimes. All groups relied on friends and family and online news as their primary sources of security information, with little concern about their social circles being potential attackers. We highlight the nuanced differences between the four at-risk groups and compare them to the broader German population when possible. We conclude by presenting recommendations for education, policy, and future research aimed at addressing the digital security needs of these at-risk user groups.

CoqQ is a framework for reasoning about quantum programs in the Coq proof assistant. Its main components are: a deeply embedded quantum programming language, in which classic quantum algorithms are easily expressed, and an expressive program logic for proving properties of programs. CoqQ is foundational: the program logic is formally proved sound with respect to a denotational semantics based on state-of-art mathematical libraries (mathcomp and mathcomp analysis). CoqQ is also practical: assertions can use Dirac expressions, which eases concise specifications, and proofs can exploit local and parallel reasoning, which minimizes verification effort. We illustrate the applicability of CoqQ with many examples from the literature.

We propose a generalization of Zhandry's compressed oracle method to random permutations, where an algorithm can query both the permutation and its inverse. We show how to use the resulting oracle simulation to bound the success probability of an algorithm for any predicate on input-output pairs, a key feature of Zhandry's technique that had hitherto resisted attempts at generalization to random permutations. One key technical ingredient is to use strictly monotone factorizations to represent the permutation in the oracle's database. As an application of our framework, we show that the one-round sponge construction is unconditionally preimage resistant in the random permutation model. This proves a conjecture by Unruh.