security analysts

Abstract: Membership inference attacks (MIAs) are the standard tool for auditing the privacy risks of machine learning models. Given a query point, an MIA aims to determine whether that point was used to train the target model. In practice, such inference must rely on the statistical signals exposed by the model's outputs, such as confidence scores, logits, and intermediate feature representations. However, existing methods often fail to efficiently summarize and combine these statistical signals. To address this limitation, we propose Pairwise Likelihood MIA (PL-MIA), a unified method that combines a Gaussian likelihood-ratio (GLR) statistic with population calibration and the Cauchy combination test. We characterize theoretically how the GLR retains variance-contraction signals and establish conditions under which population calibration and Cauchy combination improve attack power. We obtain $p$-values from pairwise comparisons between the query point and reference points not used for training, and aggregate these continuous signals using the Cauchy combination test. This preserves the evidence strength that is discarded when each pairwise comparison is reduced to a binary vote. Extensive experiments demonstrate that PL-MIA outperforms strong baselines, improving the true positive rate (TPR) by over 25\% in the critical low-false-positive regime, corroborating our theoretical findings. These results demonstrate how statistical principles can turn noisy model outputs into more powerful, calibrated, and reproducible evidence for membership privacy auditing.

Abstract: Critical-infrastructure knowledge is distributed across heterogeneous, incomplete, and weakly structured evidence, making dependency models difficult to construct automatically and difficult to trust. Large language models (LLMs) can extract structured knowledge from such evidence, but direct LLM-to-graph generation risks unsupported relationships, inconsistent terminology, incorrect entity identities, and erroneous dependency endpoints. We present an evidence-first multi-LLM framework for constructing Infrastructure Knowledge Bases (IKBs) and Infrastructure Dependency Graphs (IDGs) from heterogeneous infrastructure documentation. Multiple open-weight LLMs independently extract candidate entities and dependencies from normalized evidence, after which the framework separates evidence verification, ontology grounding, entity resolution, dependency alignment, validation, fusion, and human review. Evidence support, ontology reconciliation, endpoint resolution, model agreement, and human validation remain distinct states, while provenance and unresolved cases are preserved throughout. The validated IKB is then projected deterministically into the IDG without introducing new LLM-generated knowledge. Evaluation in nine infrastructure projects shows that entity recovery achieves substantially higher recall than complete directed dependency recovery and that canonical endpoint resolution is a major constraint in dependency construction. Cross-model overlap is also much lower for dependencies than for entities, indicating that the models often produce non-overlapping candidate assertions rather than a stable majority consensus. These findings support an auditable evidence-to-IKB-to-IDG process in which uncertainty is preserved and resolved progressively rather than collapsed into a single confidence or voting decision.

Abstract: We derive the second-order asymptotics of randomness extraction from memoryless sources with side information under security criteria based on a broad class of Csiszàr f-divergences, treating both a fixed reference side-information marginal and optimization over that marginal. The conditional varentropy decomposes into fluctuations of the conditional entropy across different values of the side information and the average variance of the conditional surprisal for each value. Without marginal optimization, these contributions yield a Gaussian-mixture second-order profile. With marginal optimization, they combine into the total conditional varentropy, yielding a single Gaussian profile. As corollaries, we obtain second-order expansions for Rényi-entropy criteria of all orders $α\in (0,1)$ and recover the known expansion for total variation distance.

Abstract: Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative training-time attacks, label flipping and backdoor poisoning, on MNIST and Fashion-MNIST with three baseline classifiers: Logistic Regression, Linear SVM, and Random Forest. Clean training is compared with poisoning rates of 5%, 10%, and 20% using clean-test accuracy, macro-precision, macro-recall, macro-F1, and, for backdoors, attack success rate. Label flipping caused clear degradation, largest for Logistic Regression and Linear SVM, while Random Forest stayed comparatively stable. Backdoor poisoning reached attack success rates from 0.9667 to 1.0000 on both datasets and all three models while often keeping clean-test performance near baseline. The results separate indiscriminate poisoning, which shows up in standard metrics, from targeted backdoor poisoning, which stays comparatively stealthy while embedding highly effective malicious behavior, and they support security-oriented evaluation beyond conventional clean-test metrics.

Abstract: The integration of Large Language Models (LLMs) into Security Operations Centers (SOCs) streamlines threat intelligence but introduces critical vulnerabilities, notably indirect prompt injection via log poisoning. Adversaries exploit this vector to execute multistep ``promptware'' kill chains by embedding malicious payloads within system logs to hijack the LLM's operational logic. Securing this pipeline presents a dichotomy: deterministic defenses are computationally efficient yet semantically blind, while purely neural evaluations introduce prohibitive latency and probabilistic flaws. To address this, we propose a novel neurosymbolic defense-in-depth architecture that ensures end-to-end pipeline integrity. The primary layer employs customized SIEM decoders as a deterministic pre-filter, performing immediate structural sanitization to neutralize volumetric padding and signature-based injections at the ingestion edge. The secondary layer leverages NeMo Guardrails to enforce strict semantic boundaries through self-checking validation on the structured SIEM alerts prior to LLM processing. Furthermore, the framework integrates a closed-loop telemetry system, providing critical Human-in-the-Loop (HITL) visibility into thwarted attacks directly within the SOC dashboard. We present a comprehensive experimental evaluation mapped to the MITRE ATLAS taxonomy, assessing the framework against diverse prompt injections. Our results demonstrate that this synergistic approach effectively dismantles the promptware kill chain - bounding LLM stochasticity with verifiable constraints, and delivering a resilient, highly observable defense mechanism for next-generation AI-SOCs.

Abstract: Logic flaws pose significant challenges in the design and implementation of modern, semantically rich systems and applications, impacting security, privacy, and trust. These flaws are inherently tied to business-specific semantics and threat models, making their discovery and reasoning difficult and hard to scale. Real-world systems often exhibit diverse application features, complex protocol logic, and domain-specific threat models, necessitating substantial human effort and domain expertise for effective security analysis. In this paper, we introduce LL-Verifier, a novel, automated framework for identifying logic vulnerabilities built on (1) large language models for autonomous modeling, and (2) logic model checkers for rigorous reasoning. LL-Verifier processes natural language inputs, in particular protocol descriptions and security goals, to automatically generate formal logic models and properties expressed in a new logic language built on a generic logic language Maude, optimized for modeling arbitrary application-level semantics. These formal models are then converted into logical state machines, enabling exhaustive, rigorous verification through logic level model checking. This approach streamlines the analysis of diverse, application-level protocols deployed in real-world scenarios, offering automated, exhaustive, and precise reasoning within their logical constraints. We evaluated the high effectiveness, efficiency, and practicality of LL-Verifier by applying it to 27 access control protocols of widely used IoT devices, which come with vendor-specific logic flows and semantics. While LL-verifier tackles a hard problem in application security, i.e., automatic logic flaws discovery, our analysis uncovers a range of sophisticated logic vulnerabilities in IoT protocols and devices with serious security and privacy implications.

Abstract: In this paper, we solve two fundamental obstacles in applying LLMs to critical domains: token efficiency and faithfulness. To address both constraints jointly, we present TEFM (Token-Efficient Faithful Modeling), a framework designed for structured data analysis in critical domains. TEFM achieves token efficiency by compressing lengthy structured observations into compact Behavioral Code tokens, dramatically reducing token consumption with minimal information loss. Moreover, TEFM enables faithful rationalization through a dual-fidelity objective that jointly optimizes code-level reconstruction and prediction-level fidelity, identifying minimal sufficient feature subsets grounded in input data. Comprehensive experiments across various domain datasets and model backbones (Qwen3, Gemma-2, Phi-4) show that TEFM achieves competitive classification accuracy with dramatic token reduction (approximately 1\% token retention in clinical and 2\% in security domains) while producing faithful rationales.

Abstract: We study scheming in LLM agents, in which agents covertly pursue misaligned goals. Our focus is to understand how scheming arises from the interaction of key factors, such as instrumental goals, environmental affordances, oversight conditions, and perceived consequences. Prior work examines only a small number of scenarios, limiting the ability to isolate how these conditions shape an agent's propensity or capability to scheme. This limited scale and task diversity also restrict coverage of realistic deployment settings and the range of scheming strategies that can be observed. To this end, we introduce SCHEMEARENA, a 400-scenario benchmark for scalable scheming stress testing, constructed through a factorized scenario synthesis framework spanning diverse safety-relevant tool domains, instrumental goals, oversight conditions, and pressure mechanisms. To enable scalable and reliable monitoring, we further propose SCOUT, a scheming monitor that grounds multi-criteria judgments in evidence drawn from agents' reasoning and actions. Across controlled stress tests on five LLM agents, we find that explicit instrumental goals are the strongest driver of scheming propensity. Strategic hints play a distinct role by helping agents translate scheming reasoning into concrete covert behavior. Oversight has mixed effects: in several closed models, action-only monitoring increases scheming, suggesting that partial oversight can act as an optimization constraint rather than a deterrent. CoT is a useful but incomplete monitoring signal: it can reveal latent scheming before execution, yet action-only scheming shows that covert behavior may occur without explicit reasoning evidence. We release the benchmark, code, and monitor at: https://github.com/launchnlp/SchemeArena.

Abstract: XR devices introduce substantial privacy concerns due to their comprehensive data collection capabilities that surpass traditional computing platforms. While existing works have demonstrated privacy concerns on Android-based XR devices such as Meta Quest series by performing network traffic analysis, little attention has been paid to the Apple Vision Pro (AVP) devices, mainly due to the closed nature and the technical challenges associated with AVP devices. In this work, we make a bold attempt to detect privacy violations of AVP applications from network traffic through automatic testing on AVP devices. Our key insight is that effective AVP application testing requires coordinated control of both cyber (software) and physical (hardware) components, which we term Coordinated Cyber-Physical Testing. Building on this insight, we design and implement AVP-Inspect, an automatic dynamic analysis framework for AVP applications, overcoming significant challenges enforced by the closed-source nature of AVP ecosystem. AVP-Inspect consists of three components: an automatic device controller by building customized hardware devices, a 3D UI explorer by designing a new exploration engine, and a privacy violation detector by constructing a unified privacy taxonomy for AVP. We first evaluated AVP-Inspect on a manually constructed ground truth dataset, then performed a large-scale analysis on 324 AVP applications downloaded from the App Store, with each app tested for 20 minutes. We found that 188 (58.0%) of apps exhibit at least one violation, and more than 60% of the network traffic flows are not properly disclosed.

Abstract: Modern cybersecurity operations rely on CTI collected from heterogeneous sources, including semi-structured threat representations, IoCs, and narrative technical reports. However, these artifacts are often insufficient in isolation to reconstruct how an attack unfolds, under which conditions each step is feasible, and which traces it leaves behind. In practice, analysts must manually correlate partial evidence scattered across multiple and only partially structured sources, delaying the design of effective prevention, detection, and response actions. To address this gap, we propose an automated pipeline that derives an actionable representation of a cyberattack from heterogeneous CTI sources. The pipeline combines a RAG architecture with a locally deployable SLM, used to consolidate such evidence and infer missing operational details. Starting from a semi-structured threat representation and auxiliary CTI documents, the pipeline produces an enriched Attack Graph that captures a coarse, tactic-aligned progression of the attack and annotates each step with explicit pre-conditions and post-conditions, and an enriched description. This representation supports prevention by exposing execution requirements, detection by highlighting observable traces, and response by clarifying the temporal progression of the attack. Then, due to the lack of validated datasets with ground-truth information on the temporal evolution of real-world attacks, we test the complete pipeline on 10 real-world case studies spanning multiple threat types, including backdoors and staged downloaders delivered via phishing. A manual assessment across 10 real-world case studies provides initial evidence that the generated graphs are consistent with expected attack progressions, indicating that the proposed approach can support analysts by consolidating dispersed CTI evidence into a structured and actionable view of attacks.