CyberSec Research

Kernel-level anti-cheats are effective against malicious player behavior in competitive video games, but raise significant user privacy concerns regarding installing unverifiable components at privileged modes (i.e., ring-0 in x86). While existing research has focused on improving the effectiveness of anti-cheats, the user privacy concern has been largely ignored. Tirith is an anti-cheat architecture that addresses this problem using two key ideas. First, instead of running video games within regular processes that players (as root admins) have control over, Tirith executes video games in Protected Virtual Machines that naturally sandbox computations from untrusted admins. Second, to monitor user behavior outside the sandbox (e.g., see if they are running malicious drivers), Tirith leverages a virtualization monitor that is trusted by both players and developers. Together, these ideas remove the need to run untrusted kernel-level anti-cheats, while providing the same level of protection compared to such solutions against a wide-range of common cheating mechanisms. The main challenge we face in implementing these ideas, however, is that the existing software stack for virtual machines is not designed to run video games and creates significant security and performance problems. We address these problems by proposing a security-focused Library OS kernel for games and an efficient graphics sharing pipeline for near-native rendering and display performance. In summary, without compromising on cheating behavior detection or performance, this work makes user privacy a first-class citizen in personal computers.

Capability-based architectures such as CHERI provide strong support for the architectural isolation of software components. To additionally protect against microarchitectural leakage, software can be written in a constant-time fashion. Modern processors, however, rely heavily on speculative execution, which can invalidate the constant-time guarantees and leak isolated secrets transiently. In this work, we show that providing secure speculation for CHERI is non-trivial, and that existing proposals fail to preserve the confidentiality guarantees. We develop a formal framework for reasoning jointly about capability safety, speculative execution, and information-flow security, and use it to demonstrate potential leaks. We then present SCHERI, a new processor design within this framework, and formally prove that it provides end-to-end secure speculation guarantees for the constant-time policy. Our results provide formal foundations and practical guidance for building future capability-based processors, which are resilient to Spectre attacks for constant-time programs.

Fully encrypted protocols (FEPs) provide encrypted channels that make all protocol-generated bytes computationally indistinguishable from uniform random strings. Several previous works have explored security definitions and constructions of unidirectional FEPs: protocols in which one party acts only as a sender, and the other acts only as a receiver. However, most applications require two-way information exchange, and a network adversary can observe communication in both directions and their shared lifetime. Because the semantics of bidirectional channels involve more complex shared state, it is possible that the ``naïve'' composition of two unidirectional channels can result in a two-way protocol that can be detected based on dependencies between the two directions, such as traffic imbalance, channel closure, failures, or connection tear-down. To address this issue, we introduce new formal security definitions for bidirectional FEPs that capture exact shaping, delivery, protocol-state integrity, private half-close, and cross-direction isolation, while revealing a public ``sending schedule'' and ``closing epoch'' that may be randomized. We show that the trivial composition fails to meet these definitions, leading to practical detection attacks. We then construct provably secure bidirectional FEPs (BiFEPs) for both the datastream and datagram settings. For datastream, we combine two direction-separated FEPs with a ``wrapper'' layer that prevents detection based on the mismatch between uni- and bi-directional connection states. For datagram, we add encrypted DATA/FIN/ACK with replay protection and loss-tolerant close. We validate the design through a Rust implementation and show that none of the surveyed deployed protocols provides the full set of BiFEP security properties.

In embodied cyber-physical systems, active cyberattacks pose an immediate threat not just to data, but to physical integrity and human safety. While existing security approaches excel at detection, they lack the runtime mechanisms to determine whether a disruption is tolerable or if performance degradation remains within safe operational bounds. This gap leaves autonomous systems vulnerable to graceful failure paralysis, where they cannot distinguish between a safe, degraded state and a catastrophic hazard during an ongoing attack. This paper presents RobResilience, an implementation of a formal resilience framework for embodied cyber-physical systems in a Webots simulation environment, using a PR2 robot and ROS2. The framework evaluates three predicates at runtime: tolerable disruption ($δ$), tolerable degradation ($γ$), and mitigation feasibility ($μ$), over a compromised device set derived from IDS confidence scores. When resilience is lost, the framework triggers available mitigation strategies. We evaluate our implementation through eight attack scenarios that systematically cover all possible combinations of the predicate state space, varying attack targets, degradation rates, and mitigation availability. Results confirm that the runtime behaviour of the implementation is consistent with the theoretical definitions.

Splash ads are full-screen advertisements that pop up and appear as the first interaction page when users start an app, often tricking users into unknowingly activating certain trigger mechanisms, such as moving the phone to redirect users to other profit-driven third parties. So far, splash ads have already caused significant real-world impacts, ranging from significantly delaying emergency response to distracting drivers, as well as degrading accessibility of apps to vision-impaired users. We analyze 108 documented implementations of advertising defenses to examine their applicability to splash ads and the requirements users face when deploying them. Our analysis identifies substantial deployment barriers, including device rooting or jailbreaking, runtime code injection, and application modification. Options without these requirements can still involve additional permissions, rule maintenance, source compilation, or payment. In our evaluation of 13 configurations of 11 tools across 10 popular apps, only one tool prevented the target ad-triggered navigation across all ten apps. It required Accessibility permission, and ads remained visible for approximately one second before dismissal. Other tested configurations failed to prevent navigation or, in some cases, left host apps unable to launch or stuck on the ad page. We further analyze the outstanding challenges and pos- sible future directions, highlighting the urgent need to incentivize smartphone manufacturers to provide more friendly and regulated platforms.

Standards bodies report cryptographic security as a single number of bits, but this value depends on the adversary cost model used to price time, memory, and quantum resources. Different conventions can therefore produce different rankings of cryptographic schemes. GAUGE represents security as a function over admissible cost models, called a security profile. Comparisons then become comparisons between profiles, and ranking reversals become an explicit structural property rather than a measurement error. We formalize price functionals over a cone of adversary cost models, show that security profiles are piecewise-linear and concave, and prove a rating trilemma: when two profiles cross, no rating can simultaneously be faithful to underlying costs, total over comparable pairs, and independent of the chosen cost model. We provide a polynomial-time linear-programming procedure that certifies whether the ranking of two schemes is robust, reverses under admissible models, or is genuinely incomparable. We extend GAUGE with a two-layer risk measure combining stochastic cryptanalytic decay with uncertainty over the appropriate cost model. We evaluate the framework on NIST post-quantum standards, classical anchors, and a 25-year chronology of cryptanalytic breaks. The analysis certifies a ranking reversal for ML-KEM-512 versus AES-128 from a 4-5% shift in memory pricing, and measures a lattice-sieving cost drift of 9.79 bits per year over eight years. A hybrid X25519 + ML-KEM-768 handshake reduces combined-break probability twenty-fold at a 2.3 kilobyte cost. The artifact reproduces all tables and figures in under seven seconds. GAUGE provides an explicit and auditable framework for reporting cryptographic security under competing cost models.

In cases where source code is not available, such as malware analysis, firmware analysis, and embedded systems analysis, vulnerability detection in compiled programs has gained importance. Current methods are heavily reliant on syntactical regularities or higher level representations that are vulnerable to changes in the compiler and may not be readily applicable to assembly code.In this article, we present SEMA-GUARD, a framework that uses semantic analysis and graph neural networks to identify flaws in assembly code. The approach improves the representation of control flow graphs by adding information about the program's execution at a lower level of abstraction, including stack manipulations, memory accesses, and data flow. A set based on the Juliet Test Suite was used to evaluate the effectiveness of SEMA-GUARD. In this set, each piece of source code is initially translated into assembly language and then broken down into function-level chunks. The suggested method, which relies only on statistical or structural data, achieves an accuracy of 85.1\% and an F1 score of 0.801, according to the results. Such results imply that including semantic information in graph-based models may be a successful method for identifying vulnerabilities in compiled code.

Wi-Fi signal data can be used to compromise the privacy of individuals. While many existing approaches rely on Channel State Information (CSI), collecting this data on typical IoT devices often requires elevated operating system permissions and specialized drivers. Consequently, this paper investigates the feasibility of utilizing Received Signal Strength Indicator (RSSI) data to predict human locations. RSSI was selected because it is accessible even on devices with limited user permissions, and therefore is more applicable to a wider array of IoT devices. To bypass the tedious process of obtaining training data needed to train an RSSI-based model, an existing Wi-Fi pose prediction project was used in this research. However, that project assumed CSI data as input. Therefore, we investigate the feasibility of cross-domain inference, i.e., feeding RSSI data into that existing CSI-based model. We collected an RSSI dataset, synchronized with video ground-truth of a person moving within a room, to evaluate the model's performance. This evaluation confirmed that RSSI data can predict locations with approximately 80% confidence when human movement is present. This demonstrates that a model trained on CSI data can be used to evaluate low-granularity RSSI data consisting of decibel-milliwatt (dBm) values to roughly locate people in the collection space. These results imply that a wide range of IoT devices can be used for privacy invasion in Wi-Fi-dense environments.

Content Management Systems (CMS), such as WordPress, power a large share of the web (~58%), and their extensibility through third-party plugins is a major source of their popularity as well as of their attack surface. One high-impact weakness that remains understudied is log file exposure by CMS plugins, which create log files for debugging or other purposes. If these files are insufficiently secured, they can disclose sensitive information (e.g. credentials, personal data) which has led to website compromises in the past. In this work, we present an agentic, LLM-based framework that automatically detects potential log file exposures in plugins of the most popular CMS (WordPress). Our agent analyzes each plugin by performing static and dynamic analysis. We evaluated our approach on the 300 most-installed WordPress plugins (about 0.6% of all), which together account for over 250M active installations, i.e. 75% of all active installations in the official plugin ecosystem. We manually validated each finding, reproducing 79 of 81 findings from 62 plugins. We observed that several protective measures appear to be implemented that we classify as creation-control (e.g. manual log activation) and access-control (e.g. deny rules in .htaccess). However, we find that multi-layered protection is required, but not always present. From these results we derive a taxonomy of log file path and protection patterns and deduce a set of best practices for developers to securely handle them. Finally, our study corroborates that agentic LLMs are an useful tool for security analysis.

We present a claim-relative evidence/reference framework for hybrid quantum-classical workflow integrity. Observational indistinguishability yields structural blind regions, distinct from finite-batch statistical misses. Within the declared lattice, a trusted same-batch scalar $R_0$ suffices for conclusion integrity, aggregate $M_0$ for aggregate plus conclusion integrity, and item-aligned binding for item identity. In 3,600 label interventions, feature/prediction views realize exact label-path invariance; all 764 geometry-aligned aggregate-blind rows equal their paired-clean responses, giving zero attack-only increment. For statistical response, the geometry-aligned construction detects 343/2,700 conclusion-changing ($τ\to 0^+$) label interventions with the conformal rule and 1,183/2,700 with the uncorrected union; the original frozen same-item geometry yields 11/2,617 and 43/2,617, respectively. The executed conformal clean false-action rates are 0.048--0.059 descriptively; its finite-sample guarantee requires exchangeability, which the overlapping-draw design violates. The cluster-preserving adaptive stress test (Gate A) reduces response versus matched controls in 25--40 of 40 environment/split cells while retaining conclusion changes. A bounded 165-design-cell ideal-statevector and finite-shot-emulation branch directly instantiates semantic, estimated and observed kernel transitions. The fixed equal-weight design estimates neither deployment prevalence nor QPU, provider or deployed-service assurance.

The Open Network (TON), with a peak market capitalization exceeding $20 billion and over 175 million activated on-chain addresses, relies on the TVM (TON Virtual Machine) to execute smart contracts. TVM uses first-class continuations with savelists to manage control flow and register state across continuation invocations. Since savelist-captured registers allow data to flow across continuation boundaries without passing through the operand stack, bytecode-level analyses cannot construct complete data flow tracking without explicitly modeling savelist semantics. We present TasmScan, the first bytecode-level static analysis framework for TVM that enables cross-continuation data flow reasoning without requiring source code. TasmScan models savelist semantics via forward register analysis with a formal over-approximation guarantee for exact-resolved save sites and locally tracked register definitions, then lifts bytecode into TASIR, a typed intermediate representation, and performs path-sensitive taint analysis with context-aware sources to detect defects. We evaluate TasmScan on 2,921 contracts from the TON verifier registry and a labeled benchmark of 208 contracts with human-confirmed ground truth. On the full corpus, TasmScan resolves 294,546 dynamic continuation targets with 100% precision; ablation confirms that savelist propagation is essential for resolving indirect register calls that depend on cross-continuation register passing. On the benchmark, TasmScan detects 95.3% of defects across five classes with 96.8% precision. A 366-pair stratified sample from the full corpus estimates 85.8% overall precision. TasmScan offers a 17x median speedup over the state-of-the-art symbolic-execution baseline, and in the path-analysis comparison completes 100% of analyses with zero crashes or timeouts.

This paper examines Smart Grid cybersecurity, emphasizing the critical distinctions between IT and OT environments. It analyzes grid architecture, substation threats, and key international standards, specifically IEC 62351, IEC 62443, and ISO 27001. Finally, it overviews latest research trends, including AI-driven threat detection.

Generative large language models (LLMs) have achieved state-of-the-art performance on many real-world tasks such as code generation and question answering. These models predominantly rely on an autoregressive decoding strategy that generates output tokens sequentially. However, their pervasive deployment raises serious privacy concerns, motivating private inference frameworks based on fully homomorphic encryption (FHE). A major limitation of existing FHE frameworks is their inefficiency in evaluating nonlinear operations, which incur substantial overhead and dominate the decode stage. In this paper, we propose ROSETTA, a hybrid CKKS/TFHE framework that overcomes this limitation. We first observe that nonlinear operations in the decode stage exhibit heterogeneous workload patterns, which can be handled effectively via a hybrid approach. We then realize this with two key contributions: 1) an adaptive segmented lookup-table protocol based on TFHE that enables efficient and accurate evaluation of nonlinear operations; and 2) a scheme-aware operator-selection framework that automatically assigns each nonlinear operator to CKKS or TFHE to minimize end-to-end decoding latency. We demonstrate that ROSETTA achieves up to $4.8\times$ Softmax speedup and $1.5$--$2.1\times$ end-to-end speedup over the SOTA framework CacheMir.

Private deep neural network (DNN) inference based on hybrid homomorphic encryption (HE) and multi-party computation (MPC) can protect user data with a formal guarantee, but at the cost of significant latency overhead due to HE. Customized HE accelerators have been proposed and have achieved orders-of-magnitude speedup for individual HE operations. However, when directly applying a commercial HE accelerator to state-of-the-art HE-MPC frameworks, we observe only limited end-to-end performance gain. This is because HE-MPC frameworks often require wireless transmission of input and output ciphertexts for each HE operation, leading to a severe network communication bottleneck. To overcome this challenge, we introduce OptiPrime, a protocol-hardware co-optimization framework for efficient private DNN inference. OptiPrime features a novel HE protocol for convolutions that substantially reduces the number of transmitted output ciphertexts and mitigates the network communication bottleneck. Meanwhile, as the new protocol introduces complex computation for fewer output ciphertext, we observe new memory access challenges due to a high volume of weight plaintexts and intermediate ciphertexts. Hence, we further propose a lightweight compression system for the weight plaintexts, reducing memory traffic by 10 times, as well as a specialized dataflow to maximize on-chip data reuse of intermediate ciphertexts. Extensive experiments show that our framework outperforms the Cheetah baseline by at most 5.7 times on CPUs and 4.2 times with an accelerator.

Local image watermarking embeds an invisible signal into selected image regions rather than spreading it across the entire image, enabling payload recovery from specific objects or regions without perceptibly altering the image. Existing studies evaluate the robustness of payload recovery and localization under image transformations, but they often focus on their own proposed method, resulting in narrow evaluations with inconsistent choices of transformations, datasets, and metrics. These inconsistencies across studies limit direct comparisons across methods and muddle the overall picture of local watermark robustness. To address this gap, we present the first systematic robustness benchmark for local watermarks across 55 image transformations, including (i) signal distortions, (ii) changes in image coordinate alignment, (iii) indirect local edits, and (iv) direct watermark edits. The benchmark evaluates MaskWM, WAM, OmniGuard, TrustMark, and PixelSeal, all methods that either provide native localization or require minimal adaptation to support it. Our results show that all evaluated methods are vulnerable to some transformation, with MaskWM standing out as offering the strongest payload recovery and localization, although it has the lowest image quality in the clean setting. Synchronization further improves MaskWM's payload recovery under several geometric transformations, albeit at an additional cost to image quality. A key finding is that local watermark robustness depends strongly on the nature of the transformation: signal distortions are often tolerated by the strongest methods, while geometric misalignment and generative local edits, such as inpainting and outpainting, can completely impair payload recovery. We observe that payload recovery and localization are related but not interchangeable, and both strongly depend on the transformation's impact on the watermark region.

Retrieval-augmented generation (RAG) systems enhance large language models (LLMs) with external knowledge but have been demonstrated to be vulnerable to corpus poisoning. Existing poisoning attacks against RAG largely focus on single-point explicit injection, where the malicious payload is fully encapsulated within a single document. Consequently, recent mitigation mechanisms have evolved to identify and diminish these threats effectively. In this paper, we first verify that existing mitigation mechanisms are insufficient for a new class of threats: indirect logic induction. Motivated by this observation, we introduce InceptionRAG, a stealthy attack mechanism that subverts the standard attack paradigm. Instead of injecting explicit malicious payloads, InceptionRAG fragments it into a chain of dormant passages. These passages appear harmless and can bypass existing mitigation mechanisms when examined separately. However, when retrieved together, they trigger LLMs to self-deduce target misinformation via multi-hop reasoning. To further improve the applicability of InceptionRAG in black-box settings, we propose zeroth-order suffix optimization (ZOSO) to automate the generation of authoritative suffixes. Extensive evaluations across three datasets and three LLMs demonstrate that InceptionRAG achieves an attack success rate exceeding 80% even under rigorous adversarial constraints. In particular, InceptionRAG shows superior evasion capabilities, effectively bypassing established defenses that mitigate traditional single-document injections. Our findings expose a concerning paradox: the stronger reasoning capabilities of LLMs increase their vulnerability to reasoning-based poisoning attacks. To mitigate potential misuse, we propose a document isolation-based defense, HODOR, which decouples adversarial logical dependencies.

Mobile agents are increasingly capable of autonomously interacting with mobile applications and performing consequential actions on behalf of users. Effective human oversight of such agents relies on a basic premise: users and agents observe consistent information from the same interface. We show that this premise can be systematically violated. Users perceive mobile interfaces through physical displays and the human visual system, making their observations subject to occlusion and luminance contrast limitations. In contrast, agents consume digital screenshots that may retain such content and accessibility representations that expose nonvisual widget metadata. The same UI state can therefore present materially different information to users and agents, a mismatch we term human-agent UI desynchronization. We investigate whether a repackaged clone of a legitimate APK can exploit this desynchronization to steer an agent toward attacker-designated actions, while remaining fully functional and behaviorally consistent with the original application for human users. We demonstrate that this threat is feasible: perturbations embedded before deployment can induce such deviations without access to runtime user instructions, agent detection or online adaptation. To systematically expose and evaluate this threat, we develop an automated framework that constructs user runtime instruction-agnostic UI desynchronization attacks and realizes them in deployable APKs. We conduct static and dynamic evaluations across five mobile-agent frameworks and three backbone models on 546 tasks involving various applications, achieving average misleading rates of 77.9% and 66.9%, respectively. A complementary questionnaire-based study with 186 participants finds that the visual perturbations used in our attacks are difficult for human users to notice.

LLM-powered autonomous agents are transforming the penetration testing space with dynamic, multi-step offensive security workflows that require minimal supervision by humans. These agents leverage sophisticated reasoning abilities and external security tools to independently carry out reconnaissance, identify vulnerabilities, devise exploitation plans, and perform post-exploitation operations. But the ability to have persistent memory, to take actions in the real world, and to do long-horizon reasoning raises qualitatively different security concerns than traditional chat-based LLM systems. Existing guardrail mechanisms for conversational AI may not be sufficient to secure autonomous AI pentesting agents accordingly. To address these issues, we carry out a comprehensive security analysis on autonomous AI-penetration testing agents. We systematically analyse representative agent architectures, characterise their trust boundaries and attack surfaces and propose a threat taxonomy that is aligned with the lifecycle and covers LLM lifecycle attacks, agent-architecture attacks and cross-cutting behavioural attacks. We analyse the limitations of existing guardrail mechanisms, identify key research gaps, and discuss future research directions for developing specialised, context-aware, and architecture-aware guardrails to secure next-generation AI-driven offensive security systems.