Cryptography and Security
Characterizing and Codifying Malware Sophistication
Angelo Porcella, Zachary Wadhams, Clemente Izurieta et al. (+2 more)
'Sophisticated' is widely used to describe malware, yet it lacks a consistent definition within academic literature. While existing software quality and complexity metrics offer some insight into malware structure, they do not capture the broader
adversarial and operational traits that contribute to real-world threat potential. This paper presents a systematization of existing approaches for assessing malware quality using static binary analysis. We define malware sophistication through a quality-focused lens by reinterpreting select characteristics from the ISO/IEC 25010 software quality standard, including reliability, maintainability, flexibility, and security, and evaluating their applicability to malware binaries. We identify which characteristics are both relevant to malware and measurable through static analysis, forming the basis for future frameworks that consistently assess malware sophistication when source code is unavailable or dynamic execution is infeasible.
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All authors Angelo Porcella, Zachary Wadhams, Clemente Izurieta, Jonathan Crussell, Ann Marie Reinhold
Cryptography and Security
Safety in Self-Evolving Agents: A Survey
Jiahao Chen, Zhou Feng, Oubo Ma et al. (+28 more)
Large
language models (LLMs) exhibit strong general capabilities, yet their parameters typically remain fixed after deployment, limiting learning from new interactions. In open-ended environments, this motivates self-evolving
agents that continually update reusable state-including model parameters,
memories, tool definitions, skills, and workflows-from data, feedback, and accumulated experience. This shift changes the safety problem: once experience becomes reusable state, past events become future causes, and information harmless in one context may later influence decisions with greater persistence, authority, or scope. Self-evolving
agent safety therefore asks not only whether a response is aligned or an action authorized, but whether safety properties survive the accumulation, generalization, and cross-context reuse of locally useful experience. We introduce SAVER, a transition-centered framework in which Substrate locates reusable influence, Adaptation captures how it changes, Violation identifies compromised safety attributes, Exposure marks where failures become observable, and Response assesses containment, repair, or revocation. Our survey reveals that failures need not originate from harmful information: legitimate state can become unsafe when adaptation expands its persistence, authority, or scope beyond the conditions under which it was valid. Existing work provides comparatively strong evidence for admission,
retrieval, activation, exposure, and local containment, but much less for descendant repair and evaluation after adaptation resumes. We therefore argue for longitudinal evaluation that traces unsafe influence to its originating transition, verifies repair across descendants, and tests whether it can re-emerge under continued
evolution.
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All authors Jiahao Chen, Zhou Feng, Oubo Ma, Yichen Yan, Ruixiao Lin, Hangtao Zhang, Linkang Du, Hengyu An, Yong Yang, Jun Liu, Junhao Li, Naen Xu, Chunyi Zhou, Yuan Su, Zehao Jin, Qianli Ma, Leyi Qi, Yiming Wang, Zhe Ma, Yuwen Pu, Mengyao Du, Yuanyi Song, Enhao Huang, Zhihui Fu, Jun Wang, Jinfeng Li, Yuefeng Chen, Hui Xue, Yiming Li, Tianyu Du, Shouling Ji
Cryptography and Security
Understanding In-Context Multimodal Jailbreaks via Posterior Reweighting
Xu Zhang, Dev Mistry, Xiang Xu et al. (+1 more)
In-context learning (ICL) jailbreaks reveal a critical vulnerability in
multimodal large
language models (MLLMs): harmful demonstrations in the
prompt can induce unsafe outputs without modifying model parameters. Despite extensive empirical evidence, existing work lacks a principled understanding of why such jailbreaks reliably succeed or how their effectiveness scales with context composition. We propose a posterior reweighting framework that models a safety-aligned MLLM as implicitly operating over competing behavioral modes, and interprets in-context demonstrations as inference-time evidence that dynamically shifts the model's posterior preference between safe and harmful behaviors. This view formalizes jailbreak as a process of evidence accumulation, yielding predictive scaling laws with respect to demonstration count, harmful ratio,
adversarial strength, and semantic diversity. Guided by this framework, we introduce a posterior-aware inference-time defense that adaptively injects benign counter-evidence based on estimated risk, effectively suppressing harmful posterior drift while preserving model utility. Compared to existing in-context defenses, our method achieves a significantly improved robustness-utility trade-off under a fixed intervention budget. Together, our results establish posterior reweighting as a unifying and predictive framework for understanding and mitigating ICL jailbreak in MLLMs.
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All authors Xu Zhang, Dev Mistry, Xiang Xu, Ren Wang
Cryptography and Security
PEARL: A Task-Aware Framework for Evaluating Differentially Private Synthetic Educational Data
Xianghui Meng, Yujing Zhang and Jionghao Lin
Personalized learning systems rely on real learner data, including performance, behavior, and demographic information, but these data are highly
privacy-sensitive. Differentially private (DP) synthetic data can support system development and educational research while reducing exposure of individual learners. Existing evaluations, however, assess
privacy and predictive usefulness separately, without determining whether synthetic learner data remain usable for the intended personalized learning task. We introduce PEARL (
Privacy-Equivalence Audit and Release Ledger), which approves a DP synthetic educational
dataset only when it passes all required checks of validity,
privacy protection, predictive usefulness, and suitability for the intended educational task, while recording why each rejected
dataset fails. Across 96 study settings, each defined by a
dataset, data-generation method,
privacy budget, and random seed, only 12 produced synthetic
datasets that passed all applicable PEARL checks. Many
privacy-protected
datasets were rejected for omitting important outcome groups, such as withdrawn students, or for failing to preserve the order of learning activities.
Fairness analysis further showed that some
datasets passing
privacy and predictive-usefulness checks still yielded unequal at-risk prediction performance across groups defined by disability and socioeconomic background. Moreover, Deep Knowledge Tracing and Self-Attentive Knowledge Tracing learned no meaningful next-response patterns from any tested synthetic knowledge-tracing
dataset, showing that
privacy protection alone does not guarantee usefulness for dropout prediction, knowledge tracing, or adaptive tutoring.
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Cryptography and Security
Black-Box Membership Inference via Word-Level Probability Estimation
Shengjie Niu, Yeheng Ge and Jian Huang
Membership inference attacks (MIAs) have emerged as critical tools for auditing
privacy risks in large
language models (LLMs), aiming to determine whether a given text was included in a model's training corpus. However, most existing MIAs require access to per-
token logits or probabilities, making them inapplicable in practice to proprietary LLMs that expose only textual continuations. To address this underexplored setting, we propose Word-level Probability MIA (WPMIA), a statistically principled MIA for strict black-box
privacy auditing. WPMIA estimates word-level generation probabilities via Monte Carlo sampling with local kernel smoothing, then aggregates these estimates into a sequence-level likelihood
estimator. Furthermore, WPMIA constructs the likelihood conditioned on different prefixes, thereby amplifying the distributional differences between members and non-members. We evaluate WPMIA across various open-source LLMs and find that it consistently outperforms existing black-box baselines. Importantly, we also evaluate WPMIA on modern proprietary LLMs, including GPT-5-Chat, Gemini-2.5-Flash, and Claude-4.5-Haiku, achieving an average TPR@5%FPR of 42.0 across these models. These results offer a sound foundation for future research on strict black-box membership inference. Code is available at \href{https://github.com/niusj03/WPMIA}{https://github.com/niusj03/WPMIA}.
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Cryptography and Security
Adaptive Diffusion Freezing: Privacy-preserving Diffusion Models Against Membership Inference Attacks
Jialu Guo, Xiao Han and Junjie Wu
Diffusion models have achieved remarkable success in generative tasks across various areas, however their training process raises significant
privacy concerns, particularly under membership inference attacks (MIAs). Prior studies on
privacy-preserving of
diffusion models fail to balance
privacy, utility, and efficiency. To address this gap, we propose a novel framework of
privacy-preserving
diffusion models, Adaptive
Diffusion Freezing (ADF), which can defend against MIAs with better trade-off. By leveraging cross-timestep adaptive freezing training, ADF explicitly control the participation of different data subsets across
diffusion timesteps via a mask
matrix, which reduces the over-memorization and leads to more uniform model behaviors between member and nonmember samples. To construct a freezing mask
matrix that effectively reduce membership leakage without unnecessarily harming generation quality, we introduce a pretraining-based risk-aware freezing policy to estimate MIA risk based on memorization tendency, and suppress the contribution of the subset-timestep pairs with higher risk. Evaluations on multiple
datasets demonstrate that ADF provides effective defense performance as well as state-of-the-art
privacy-utility-efficiency trade-off performance compared to various baselines.
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Cryptography and Security · Cross-listed from quant-ph
When Measurement Constraints Favor Quantum Computational Sensing for Stealthy Power-Grid Attack Detection
Saisubramaniam Gopalakrishnan, Supreeth B S, Pranav Sarda et al. (+2 more)
Power-grid defenses that rely on digital telemetry remain vulnerable to stealthy attacks that preserve plausible reported states while altering the underlying physical system. We study when Nitrogen-Vacancy (NV) sensing provides a useful independent physical channel, and when coherent processing before measurement adds value. Across IEEE 14-, 30-, and 118-bus simulations with Lindblad NV models, we evaluate standard FDIA, BDD-stealth, statistical-stealth, and concealed
topology attacks. The results reveal an observability hierarchy: evidence shifts from digital telemetry, to reported-versus-physical consistency, to the physical NV state.
Quantum Computational Sensing (QCS) follows this selectivity, becoming informative only when the physical state itself carries attack evidence. We then compare QCS, conventional 4-setting NV readout, and
tomography under matched measurement budgets. At a matched total budget of only 40 physical trials per sensor on case14, QCS reaches AP $0.944$, versus 0.800 for conventional NV readout and 0.751 for
tomography; the same low-budget ordering holds on case30 and case118. Multi-setting methods recover as additional measurements become affordable, showing that the QCS benefit is a measurement-efficiency advantage rather than a universal accuracy advantage. Finally, we ask where the benefit varies across the three case simulations. Although the
quantum-to-classical Fisher-information ratio increases from 1.21x to 1.54x, Normal–Attack Helstrom separation collapses in the harder regimes, and interleaved control substantially increases that separation only on case14, thereby
quantum sensitivity does not necessarily imply task-relevant distinguishability. Our results show that realized QCS utility depends on the full chain from physical perturbation to state separation, coherent processing, and measurement under the resource constraints of the task.
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All authors Saisubramaniam Gopalakrishnan, Supreeth B S, Pranav Sarda, Ashesh Xalxo, Dagnachew Birru
Cryptography and Security · Cross-listed from stat.ME
Differentially Private Average Treatment Effect Estimation by Propensity Score Blocking
Duncan Stewardson, Grayson W. White and Adam Groce
Average treatment effect (ATE) estimation in observational studies is a fundamental statistical tool used frequently in social science, medicine, and other fields. These fields often work with sensitive data where
privacy protections are important, so a differentially private mechanism for ATE estimation is highly desirable. Here we present two propensity score-based
algorithms for ATE estimation on observational data, one improving the inverse probability weighting (IPW) method used in prior work, and the other using blocking on the propensity score (BPS). Both show lower error and less bias than prior work, with the BPS-based
algorithm frequently reducing error by 75% or more compared to prior work.
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Cryptography and Security · Cross-listed from eess.SP
Adaptive Distributed Physical-Layer Authentication and Attack Detection in 6G Non-Terrestrial Networks via Causal Meta-Learning
Parsa Rajabi, Mohammad Reza Abedi, Nader Mokari et al. (+2 more)
Physical-layer authentication (PLA) in non-terrestrial networks (NTNs) is challenged by severe Doppler shifts, long delays, and fast channel variations, which cause distribution shifts and degrade conventional learning methods. Existing PLA schemes often rely on single features or generalize poorly to unseen environments. This paper proposes a secure adaptive framework for authentication in multi-zone networks (SAFA-MZ), a causal meta-learning framework for distributed PLA (DPLA) in NTNs. First, we design a multi-feature fingerprint that combines spatial, angular, combiner, subspace, and Doppler-delay features. The fingerprint is adaptive and distributed, as it fuses heterogeneous physical-layer features and measurements from multiple aerial nodes. Second, we formulate a structural causal model (SCM) to capture the relations among design choices, environmental factors, extracted features, and authentication outcomes. Third, we develop a model-agnostic meta-learning (MAML) strategy with invariant risk minimization (IRM) and causal consistency regularization for fast adaptation to unseen NTN environments with few labeled samples. Fourth, we propose a two-stage authentication scheme that performs local recognition and activates time-difference-of-arrival (TDOA) localization with a graph
attention (GAT) network only when needed, which reduces backhaul overhead. Simulations show that SAFA-MZ achieves 92% accuracy and 96% AUC, outperforming centralized deep learning and single-feature baselines across diverse environments.
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All authors Parsa Rajabi, Mohammad Reza Abedi, Nader Mokari, Paeiz Azmi, Halim Yanikomeroglu
Cryptography and Security
Audio Deepfake Detection Using Temporal Coherence Analysis
Justin D. Norman and Sarah Barrington
The proliferation of AI-generated audio (so-called "deepfake" audio) poses significant threats to information integrity, from voice cloning fraud to synthetic music copyright disputes. We present a temporal coherence analysis framework built upon Contrastive
Language-Audio Pretraining (CLAP)
embeddings that spans
speech, instrumental music, and music with vocals. By computing pairwise cosine similarities between audio segment
embeddings and extracting statistical features from the resulting distributions, we train lightweight ensemble classifiers that reliably distinguish authentic from synthetic audio. Our work provides an interpretable, computationally efficient alternative to common deep learning methods while still achieving competitive performance across
speech and music domains. Further, we reveal two notable empirical findings about audio deepfakes: (1) a feature-label inversion phenomenon in which 21 of 29 statistical features reverse their discriminative direction between training and in-the-wild deployment, and (2) a
speech–music direction reversal in which
entropy discriminates in opposite directions for
speech and music deepfakes.
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Cryptography and Security
Concept drift mitigation through community and spectral graph analysis for the detection of cyberattacks in network traffic
Julien Michel, Abdul Qadir Khan, Majed Jaber et al. (+1 more)
In network traffic, legitimate behaviours and attack techniques evolve jointly - the phenomenon known as 'concept drift' [1]. Every detector is thereby left obsolete between two updates, and always one step behind adversaries. In this work, we propose to move the point of intervention from the model, repaired after the drift, to the feature space, selected before learning. We therefore introduce t-robustness, a stability score defined for each feature independently of any detection model, comparable across an entire feature space. It combines the step-by-step distance between successive statistical states of a feature, and its cumulative divergence from its initial state, so that a slow monotonic drift cannot pass for stability. The candidates are drawn from abnormal network connectivity patterns left by scans, DoS and communications between endpoints, read through graph community metrics and spectral metrics. The evaluation is performed on the UGR16
dataset, across three learning scenarios and a control scenario, as well as without model update, and demonstrate that t-robust feature spaces sustain detection where the baselines collapse: retained expectancy at the last test interval reaches 0.6025, against 0.5230 for graph community features and 0.3831 for the base NetFlow features.
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All authors Julien Michel, Abdul Qadir Khan, Majed Jaber, Pierre Parrend
Cryptography and Security
FPGA Acceleration of Fully Homomorphic Encryption with Adaptive Key Switching
Zhihan Xu, Jayashree Adivarahan, Rajgopal Kannan et al. (+1 more)
Fully Homomorphic Encryption (FHE) enables
privacy-preserving cloud services but incurs substantial computation overhead, making hardware acceleration essential. Among FHE operations, key-switching is a major performance bottleneck. Recent cryptographic advances introduce a novel key-switching method (i.e., KLSS) that reduces certain operational complexity but demands higher computational precision than the traditional Hybrid Key Switching (HKS) method. This trade-off leads to distinct computation and
memory requirements, making the relative latency of KLSS and HKS highly dependent on hardware parallelism, FHE security parameters, and available on-chip
memory capacity, particularly on FPGA platforms, where
memory resources and parallelism must be carefully balanced. In this work, we first propose a
memory-efficient KLSS datapath that eliminates off-chip ciphertext transfers. We then develop a performance model to analyze and compare the overheads of both KLSS and HKS. Our analysis reveals that an adaptive solution supporting both methods can achieve lower overall latency than a static method during FHE computation. Guided by the performance model, we design an adaptive FPGA-based FHE accelerator that dynamically selects between HKS and KLSS during computation. We implement the accelerator on an Alveo U280 and evaluate it across multiple FHE
benchmarks. Experimental results demonstrate that our adaptive solution achieves a 1.84-3.31$\times$ speedup in bootstrapping latency and a 1.66-2.52$\times$ speedup in secure image classification compared to state-of-the-art FPGA accelerators.
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All authors Zhihan Xu, Jayashree Adivarahan, Rajgopal Kannan, Viktor K. Prasanna
Cryptography and Security
DuplexJail: Spoken Interruption Attacks on Full-Duplex Speech Models
Jaechul Roh, Deepak Chandran, Amir Houmansadr et al. (+1 more)
Full-duplex
speech models accept user
speech while generating responses, making input timing a potential safety concern. We introduce DuplexJail, which delivers fixed, request-independent spoken jailbreak
prompts through the user audio channel. Across four open-source models and 720 harmful requests from AdvBench and HarmBench, we compare fixed-delay and refusal-triggered interruption with request-end and post-response controls. On AdvBench, Guided Completion at a 1.0 s delay raises whole-response attack success rates to 40.3% for PersonaPlex and 48.7% for PersonaPlex-RL, increases of 33.8 and 39.3 percentage points over baseline. On HarmBench, which was not used for
prompt selection, the same
prompt at a 0.5 s delay increases ASR by 14.7 and 12.3 points, respectively. Effects vary across models: selected conditions increase FLM-Audio's harmfulness, while BayLing-Duplex shows decreases. These results show that spoken-jailbreak effectiveness depends on delivery timing and motivate safety evaluation across stages of full-duplex interaction.
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All authors Jaechul Roh, Deepak Chandran, Amir Houmansadr, Andrea Fanelli
Cryptography and Security
An Experimental Evaluation of Multimodal Prompt Injection Attacks on Agentic AI Frameworks
Viet K. Nguyen and Mohammad I. Husain
Agentic AI frameworks let a
language model plan, keep
memory, and call tools that reach real files, mail, and services. Most of these
agents also read images, which gives an attacker a way to put text into the
agent's context without going through the user. We present MMPIBench, a reproducible
benchmark that measures what happens next. It delivers a fixed set of attacks through six visual carriers (OCR text, overlays, EXIF metadata, QR codes, fake interfaces, and hybrids) and records how far each injected instruction travels through the
agent, from perception through planning to the tool call. Across 720 runs covering six frameworks, five foundation models, six carriers, and four attacker objectives, attacks complete in approximately 1% of runs but are attempted in 12.8%, and the gap is closed almost entirely at the planning step, where the model reads the injected instruction and declines to act on it. The model matters far more than the framework for whether an instruction is acted on. One model never attempts an attack and recognizes the injection in 59.7% of runs, while two others attempt in 23.6%. We then extend the
benchmark to audio, the only other raw perceptual channel current frontier models accept. Only two of the five models ingest audio and only three of the six frameworks deliver it, but where the signal arrives the attack completes in 49% of
cells, and in 75% for one model. Reporting completion alone therefore understates exposure, and perceptual channels beyond
vision are narrower but much less defended.
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Cryptography and Security
X-amine509: Predicting the Practical Risk Level of Enterprise X.509 Certificates
Cameron Keith, Shubh Patel, JD Kilgallin et al. (+1 more)
Enterprises managing large X.509 certificate inventories face a prioritization problem: deterministic analysis tools that precisely identify standards violations are indispensable for remediation, but applying them exhaustively across millions of certificates is operationally impractical. We present X-amine509, a two-stage triage system that uses machine learning to rapidly rank certificates by predicted risk and route only the highest-risk items to full deterministic analysis. Certificate risk is quantified as a composite score derived from 177 defect checks grounded in CA/Browser Forum Baseline Requirements, NIST IR 8547/SP 800-57, and cryptographic strength criteria, weighted by security severity across four tiers ranging from cryptographic breaks to minor compliance deviations. We collected 1,027,714 X.509 certificates from Fortune 500, .gov, and .edu domains and scored each using this rubric. On a held-out test set of 201,976 certificates, our best model (Extra Trees) achieves $R^2$ of 0.993 with MAE of 2.26, while Decision Tree scores $R^2$ of 0.986 at 3.7 million certificates per second on a single machine. Ranking quality confirms the triage value: aggregate NDCG exceeds 0.997, and severity-tier classification reports 99.76% accuracy with 98.90% recall on critical-tier defects. Thirteen months later, we retrieved another 571,374 certificates to test our models' durability over time, and the Extra Trees and Decision Tree models maintain MAE below 6.8, $R^2$ of at least 0.915, aggregate NDCG above 0.988, severity-tier accuracy of at least 99.52%, and critical-tier recall of at least 97.03%. Feature importance analysis identifies validity period, Extended Key Usage configuration, negative serial number encoding, and self-signed status as the strongest risk predictors, providing coarse interpretability at the triage stage.
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All authors Cameron Keith, Shubh Patel, JD Kilgallin, Caleb Shorter
Cryptography and Security
LLMSec-AV: A Vulnerability Taxonomy and LLM-Driven Software Weakness Discovery Framework for Autonomous Vehicles
Md. Wasiul Haque, Sagar Dasgupta and Mizanur Rahman
Automated vehicles rely on millions of lines of safety-critical software, yet general-purpose analyzers do not understand which code can affect vehicle motion. This study asks whether large
language models (LLMs) with explicit automated-vehicle (AV) security knowledge improve weakness detection beyond rule-based tools. We developed an AV vulnerability taxonomy with 18 weakness classes from vulnerability records, security advisories, and AV-security literature, and integrated it into LLM-based Security Analysis for Automated Vehicles (LLMSec-AV). Evaluated on Autoware, the framework decomposed 770 translation units into 4,673 functions and analyzed 161 functions under four prompting conditions involving taxonomy context,
retrieval from 374 prior disclosures, and multi-step analysis. Findings were compared with 46 weakness locations mined from upstream fixes and a flag-volume-matched permutation baseline. CodeQL, Semgrep, cppcheck, and the Clang Static Analyzer evaluated the same code, with AV-specific rules added to CodeQL and Semgrep. Generated fuzzing harnesses were tested using AFL++ and sanitizers. LLM conditions recovered up to 76% of the 46 known weakness locations, outperforming conventional analyzers. CodeQL, Semgrep, and the Clang Static Analyzer matched none, while cppcheck matched one despite 1,301 alerts. Unaided prompting achieved similar detection performance, showing that the taxonomy did not drive recall. However, taxonomy context increased the share of findings assigned to a weakness class from near zero to over 80%, improving interpretability and triage. Six of the 18 classes could not be directly represented as static-analysis rules. LLMSec-AV introduces an AV-specific, machine-readable vulnerability taxonomy for weakness discovery and shows that LLMs can complement conventional analyzers by identifying and organizing safety-relevant findings in real AV software.
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Cryptography and Security
Encrypt What Matters: When Selective Homomorphic Inference Is Efficient
Ali Backour, Juan Reyes, Jaime Punyed et al. (+1 more)
Fully homomorphic encryption (FHE) enables inference on private data without revealing it to the server, but evaluating an entire input under FHE is expensive. We study selective homomorphic inference, where only a sensitive region of interest (ROI) is encrypted, and computations independent of that region are performed in plaintext. Selective evaluation produces the same output as full FHE on the same model, without retraining. Its efficiency depends on how quickly encrypted dependencies spread through the network. For small encrypted ROIs, locality-preserving architectures can achieve order-of-magnitude homomorphic-evaluation speedups, whereas architectures with early global mixing provide essentially no speedup. These results identify locality as the key architectural property governing the benefit of selective homomorphic inference.
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All authors Ali Backour, Juan Reyes, Jaime Punyed, Ana Onoprishvili
Cryptography and Security
Cross User/App Network Attacks - Hijacking TCP Connections and DNS Cache Poisoning via a Malicious User/App (Extended Version)
Tamir Shahar and Amit Klein
Off-path network attacks against TCP and DNS (over UDP) client-server connections are generally considered impractical nowadays, due to built-in security features in these protocols, e.g. randomized TCP (initial) sequence numbers and randomized UDP source ports, respectively. In this work, we refute this presumption by demonstrating that an unprivileged malicious application running on the client (but practically off-path), when combined with a remote off-path adversary, can enable powerful network attacks against such connections. We show how such a local–remote collaboration between the malicious application and a remote adversary allows inference of sensitive connection state, including TCP sequence numbers and DNS stub-resolver UDP source ports. Our attacks exploit standard socket API calls such as bind(), protocol mechanisms such as IP options, and operating system features such as cBPF and procfs to infer the TCP initial sequence number (ISN) and the UDP source port in use by the connection of interest. Specifically, we take advantage of certain properties of the ISN generation
algorithm as implemented in major operating systems. We demonstrate TCP connection hijacking in Linux, Android, Windows, macOS and iOS, and DNS cache poisoning against Windows, Android and the popular systemd-resolved DNS stub resolver in Linux. We evaluate our techniques across multiple operating systems and realistic deployment settings, including environments behind port-preserving NAT-integrated routers. We disclosed our techniques to Microsoft, Apple, Linux and Google, which led to the release of several patches.
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