Papers

Embedded devices face an ever-expanding threat landscape: vulnerabilities in application software, operating system kernels, and peripherals threaten the embedded device integrity. Existing computer-architectural defenses fully consider at most two of these threat vectors in their security model. This paper aims at addressing this gap using a novel capability architecture. To this end, we combine a token capability approach suitable for building an untrusted operating system with protection against malicious devices without requiring hardware changes to peripherals. First, we develop and evaluate a full FPGA implementation of our capability architecture around legacy hardware components. Further, we present a soft real-time operating system based on Zephyr that has no run-time software TCB. To this end, we disaggregate Zephyr's subsystems into small, mutually isolated components. All subsystems that exist at run time, including scheduler, allocator and DMA drivers, and all peripherals are fully untrusted. We believe that our work offers a foundation for more rigorous security-by-design in tomorrow's security-critical embedded devices.

Modern vision-language-model (VLM) based graphical user interface (GUI) agents are expected not only to execute actions accurately but also to respond to user instructions with low latency. While existing research on GUI-agent security mainly focuses on manipulating action correctness, the security risks related to response efficiency remain largely unexplored. In this paper, we introduce SlowBA, a novel backdoor attack that targets the responsiveness of VLM-based GUI agents. The key idea is to manipulate response latency by inducing excessively long reasoning chains under specific trigger patterns. To achieve this, we propose a two-stage reward-level backdoor injection (RBI) strategy that first aligns the long-response format and then learns trigger-aware activation through reinforcement learning. In addition, we design realistic pop-up windows as triggers that naturally appear in GUI environments, improving the stealthiness of the attack. Extensive experiments across multiple datasets and baselines demonstrate that SlowBA can significantly increase response length and latency while largely preserving task accuracy. The attack remains effective even with a small poisoning ratio and under several defense settings. These findings reveal a previously overlooked security vulnerability in GUI agents and highlight the need for defenses that consider both action correctness and response efficiency. Code can be found in https://github.com/tu-tuing/SlowBA.

Detecting and responding to cyber attacks is increasingly difficult as high-volume, complex network traffic allows threats to remain concealed. While Intrusion Detection Systems (IDSs) identify anomalous behavior, Attack Graphs (AGs) serve as the primary threat model for analyzing attacker strategies and informing any response. Despite the conceptual connection being recognized in early research, the field of AG and IDS integration lacks a common structure. This paper presents the first systematic analysis of AG-IDS integration, reviewing a 73 comprehensive works in literature. We introduce a novel taxonomy revealing that current research is dominated by specialized, single-purpose integrations, such as using AGs to filter IDS false positives or using IDS alerts to prune AGs. Our analysis highlights a critical gap: the absence of a unifying framework that treats IDSs and AGs as a cohesive, integrated system. To address this gap, we propose a formal AG-IDS lifecycle. This framework establishes a continuous feedback loop where IDSs refine the accuracy of AG models, and those updated models, in turn, enhance IDS detection capabilities. We provide a proof-of-concept implementation demonstrating the practical advantages of this lifecycle for threat detection and incident response. Finally, we conclude by elaborating on significant opportunities for future development within the AG-IDS domain.

Aircraft engine blade maintenance relies on inspection records shared across manufacturers, airlines, maintenance organizations, and regulators. Yet current systems are fragmented, difficult to audit, and vulnerable to tampering. This paper presents BladeChain, a blockchain-based system providing immutable traceability for blade inspections throughout the component life cycle. BladeChain is the first system to integrate multi-stakeholder endorsement, automated inspection scheduling, AI model provenance, and cryptographic evidence binding, delivering auditable maintenance traceability for aerospace deployments. Built on a four-stakeholder Hyperledger Fabric network (OEM, Airline, MRO, Regulator), BladeChain captures every life-cycle event in a tamper-evident ledger. A chaincode-enforced state machine governs blade status transitions and automatically triggers inspections when configurable flight hour, cycle, or calendar thresholds are exceeded, eliminating manual scheduling errors. Inspection artifacts are stored off-chain in IPFS and linked to on-chain records via SHA-256 hashes, with each inspection record capturing the AI model name and version used for defect detection. This enables regulators to audit both what defects were found and how they were found. The detection module is pluggable, allowing organizations to adopt or upgrade inspection models without modifying the ledger or workflows. We built a prototype and evaluated it on workloads of up to 100 blades, demonstrating 100% life cycle completion with consistent throughput of 26 operations per minute. A centralized SQL baseline quantifies the consensus overhead and highlights the security trade-off. Security validation confirms tamper detection within 17~ms through hash verification.

The recovery of types from stripped binaries is a key to exact decompilation, yet its practical realization suffers. For composite structures in particular, both layout and semantic fidelity are required to enable end-to-end reconstruction. Many existing approaches either synthesize layouts or infer names post-hoc, which weakens downstream usability. This is further aggravated by an excessive runtime overhead that is especially prohibitive in automated environments. We present XTRIDE, an improved n-gram-based approach that focuses on practicality: highly optimized throughput and actionable confidence scores allow for deployment in automated pipelines. When compared to the state of the art in struct recovery, our method achieves comparable performance while being between 70 and 2300 times faster. As our inference is grounded in real-world types, we achieve the highest ratio of fully-correct struct layouts. With an optimized training regimen, our model outperforms the current state of the art on the DIRT dataset by 5.09 percentage points, achieving 90.15% type inference accuracy overall. Furthermore, we show that n-gram-based type prediction generalizes to function signature recovery: conducting a case study on embedded firmware, we show that this efficient approach to function similarity can assist in typical reverse engineering tasks.

Enterprise adoption of cloud-based AI agents faces a fundamental privacy dilemma: leveraging powerful cloud models requires sharing sensitive data, while local processing limits capability. Current agent frameworks like MCP and A2A assume complete data sharing, making them unsuitable for enterprise environments with confidential information. We present SplitAgent, a novel distributed architecture that enables privacy-preserving collaboration between enterprise-side privacy agents and cloud-side reasoning agents. Our key innovation is context-aware dynamic sanitization that adapts privacy protection based on task semantics – contract review requires different sanitization than code review or financial analysis. SplitAgent extends existing agent protocols with differential privacy guarantees, zero-knowledge tool verification, and privacy budget management. Through comprehensive experiments on enterprise scenarios, we demonstrate that SplitAgent achieves 83.8% task accuracy while maintaining 90.1% privacy protection, significantly outperforming static approaches (73.2% accuracy, 79.7% privacy). Context-aware sanitization improves task utility by 24.1% over static methods while reducing privacy leakage by 67%. Our architecture provides a practical path for enterprise AI adoption without compromising sensitive data.

The Internet of Things (IoT) has revolutionized the way devices communicate and interact with each other, but it has also created new challenges in terms of security. In this context, intrusion detection has become a crucial mechanism to ensure the safety of IoT systems. To address this issue, a comprehensive comparative study of advanced techniques and types of IoT intrusion detection systems (IDS) has been conducted. The study delves into various architectures, classifications, and evaluation methodologies of IoT IDS. This paper provides a valuable resource for researchers and practitioners interested in IoT security and intrusion detection.

We propose a bipartite quantum key distribution (QKD) protocol based on causal nonseparability: the presence of a resource – a process matrix – that does not correspond to any definite causal order between two parties. In our protocol, Alice and Bob perform local operations arranged in a “causal-order guessing game,” whereby each round yields an 85.35% probability of matching bits when the communication is undisturbed. This raw matching probability (or equivalently, a $\sim14.65\%$ error rate) is amenable to standard forward error-correction strategies. We further discuss the practical construction of the QKD protocol using indefinite causal order, where several different scenarios are deeply analyzed.

Converting a large C++ code base (800k lines of code) into Java alone is challenging. Changing the architecture from a monolith into an application adhering to the Java application server standard and to run it on WildFly is a different number. This report describes the experience made during the C++ to Java conversion, the techniques used as well as the way to success of running the Java code on the application server for the first time. The approaches to solve the usual C++ to Java culprits, like multiple inheritance, enum-handling and scoped objects are described. A clang-tool-based software is developed to continuously regenerate the Java, because development on the C++ code base continued.

This paper tackles the discovery of tMEV, that is, the Maximal Extractable Value on blockchains that arises from Token smart contracts. This scope differs from the existing MEV-discovery research, which analyzes application-layer contracts or attacker contracts, but ignores the wide and diverse range of token contracts. This paper presents a pipeline of techniques for tMEV discovery, including tSCAN, a static analysis tool for identifying non-standard supply-control functions in token contracts, and tSEARCH, a searcher that uncovers profitable tMEV opportunities by generating, refining, and solving token-specific constraints. By replaying real-world transactions, this paper demonstrates both the profitability of tMEV strategies and existing searchers' unawareness of them: the proposed tSEARCH extracts $10\times$ more profit than observed MEV activity on Ethereum. The practicality of tMEV searching is demonstrated through a prototype built on Slither, showing high effectiveness with low performance overhead.

In post-quantum blockchain settings, objects that require validity proofs (e.g., blob roots, execution-layer or consensus-layer signature aggregates) must be broadcast through mempool and relay networks. Recursive STARKs have been proposed to aggregate such proofs so that each node forwards one proof per tick plus objects without proofs, capping per-node proof bandwidth at roughly 128 KB degree per tick. We observe that propagation does not inherently require validity proofs on the path-only a lightweight assurance that an object is eligible for relay. We present AR-ACE (ACE-GF-based Attestation Relay for PQC), in which relay nodes forward objects plus compact attestations (e.g., identity-bound signatures or commitments) and do not generate, hold, or forward any full validity proof. Only the builder (or final verifier) performs a single aggregated validity proof over the set of objects it includes. This proof-off-path design removes proof overhead from the propagation path entirely, yielding an order-of-magnitude reduction in proof-related relay bandwidth relative to proof-carrying propagation. When instantiated with ACE-GF-derived attestation keys, AR-ACE preserves a unified identity story with on-chain authorization and is PQC-ready. We specify a protocol model, state design goals and security considerations, define security games, and provide a structural bandwidth comparison with recursive-STARK-based propagation.

Post-quantum signature schemes impose kilobyte-scale on-chain artifacts. Verifying them inside ZK circuits merely relocates the cost via expensive lattice arithmetic in prover circuits. We present ZK-ACE (Zero-Knowledge Authorization for Cryptographic Entities), which replaces transaction-carried signature objects with identity-bound ZK statements. Given a deterministic identity derivation primitive (DIDP) as a black box, the prover demonstrates in zero knowledge that an identity consistent with an on-chain commitment authorized the transaction; no signature object is produced or verified on-chain. We provide game-based definitions and reduction-based proofs for authorization soundness, replay resistance, substitution resistance, and cross-domain separation, under knowledge soundness, collision resistance, and DIDP recovery hardness. Structural data accounting shows an order-of-magnitude reduction in per-transaction authorization data versus direct PQC deployment. A reference implementation offers two backends: Circle STARK (341 active rows / 361 AIR constraint expressions, 14.5 ms prove, 1.1 ms verify, approx. 107 KB proofs, transparent setup, post-quantum-oriented) and Groth16/BN254 (2,155 R1CS constraints, 37.3 ms prove, 128-byte proofs). Both are roughly 500–2,300x smaller than in-circuit PQC signature verification. Under mandatory per-block STARK aggregation, per-transaction consensus-visible data is approx. 160 bytes.

We present Lattice (L, ticker: LAT), a peer-to-peer electronic cash system designed as a post-quantum settlement layer for the era of quantum computing. Lattice combines three independent defense vectors: hardware resilience through RandomX CPU-only proof-of-work, network resilience through LWMA-1 per-block difficulty adjustment (mitigating the Flash Hash Rate vulnerability that affects fixed-interval retarget protocols), and cryptographic resilience through ML-DSA-44 post-quantum digital signatures (NIST FIPS 204, lattice-based), enforced exclusively from the genesis block with no classical signature fallback. The protocol uses a brief warm-up period of 5,670 fast blocks (53-second target, 25 LAT reduced reward) for network bootstrap, then transitions permanently to 240-second blocks, following a 295,000-block halving schedule with a perpetual tail emission floor of 0.15 LAT per block. Block weight capacity grows in stages (11M to 28M to 56M) as the network matures. The smallest unit of LAT is the shor, named after Peter Shor, where 1 LAT = 10^8 shors.

Control of encrypted digital assets is traditionally equated with permanent possession of private keys, a model that precludes regulatory supervision, conditional delegation, and legally compliant transfer at the cryptographic layer. Existing remedies (multi-signature schemes, threshold signatures, smart contracts, custodial delegation) require persistent key exposure, on-chain state mutation, or trusted intermediaries. We introduce Condition-Triggered Dormant Authorization Paths (CT-DAP), a cryptographic asset control method built on destructible authorization factors and parameterized by a root-derivable framework satisfying deterministic key derivation, context-isolated capability generation, and authorization-bound revocation. Under CT-DAP, control rights are dormant authorization paths composed of user-held credentials and administrative factors held by independent custodians; a path remains cryptographically inactive until all factors are simultaneously available. Upon verification of predefined conditions (e.g., user consent, inheritance events, time-based triggers), the corresponding factor is released, activating the path. Revocation is achieved by destroying factors, rendering the path permanently unusable without altering the cryptographic root. We formalize the threat model, define security games for unauthorized control resistance, path isolation, and stateless revocation, and prove security under standard assumptions (AEAD security of AES-GCM-SIV, PRF security of HKDF, memory-hardness of Argon2id, collision resistance of SHA-256). We instantiate CT-DAP using the Atomic Cryptographic Entity Generative Framework (ACE-GF) and evaluate performance, demonstrating sub-second activation latency with configurable security-performance trade-offs.

The Cyber Security and Resilience (Network and Information Systems) Bill, introduced to Parliament in November 2025, represents the most significant reform of UK cyber security legislation in nearly a decade. This paper provides a comprehensive practitioner-oriented analysis of the Bill's provisions, their practical implications, and the steps organisations must take to achieve compliance. It examines the expanded regulatory scope covering managed service providers, data centres, and designated critical suppliers; the enhanced 24/72-hour incident reporting regime; the strengthened enforcement architecture including penalties of up to \pounds17 million or 4% of worldwide turnover; and the Secretary of State's new executive powers. The paper compares the Bill with the EU's NIS2 Directive and DORA, proposing a practical dual-compliance framework for financial services firms. It explains how Zero Trust Architecture principles can serve as a foundation for meeting the Bill's requirements, and how the NCSC's Cyber Assessment Framework v4.0 provides the assurance pathway. Four detailed appendices provide entity-specific compliance roadmaps, worked case studies mapping real UK incidents to Bill provisions, sector-specific action plans for financial services, energy, health, and MSPs, and a complete gap analysis and self-assessment tool mapped to CAF v4.0 and the Bill's requirements.

This paper contributes to the nascent debate around safety cases for frontier AI systems. Safety cases are structured, defensible arguments that a system is acceptably safe to deploy in a given context. Historically, they have been used in safety-critical industries, such as aerospace, nuclear or automotive. As a result, safety cases for frontier AI have risen in prominence, both in the safety policies of leading frontier developers and in international research agendas proposed by leaders in generative AI, such as the Singapore Consensus on Global AI Safety Research Priorities and the International AI Safety Report. This paper appraises this work. We note that research conducted within the alignment community which draws explicitly on lessons from the assurance community has significant limitations. We therefore aim to rethink existing approaches to alignment safety cases. We offer lessons from existing methodologies within safety assurance and outline the limitations involved in the alignment community's current approach. Building on this foundation, we present a case study for a safety case focused on Deceptive Alignment and CBRN capabilities, drawing on existing, theoretical safety case "sketches" created by the alignment safety case community. Overall, we contribute holistic insights from the field of safety assurance via rigorous theory and methodologies that have been applied in safety-critical contexts. We do so in order to create a better foundational framework for robust, defensible and useful safety case methodologies which can help to assure the safety of frontier AI systems.

Knowledge distillation from proprietary LLM APIs poses a growing threat to model providers, yet defenses against this attack remain fragmented and unevaluated. We present DistillGuard, a framework for systematically evaluating output-level defenses against LLM knowledge distillation. We introduce a taxonomy of three defense categories – output perturbation, data poisoning, and information throttling – and evaluate nine defense configurations using a standardized pipeline with Qwen3-14B as teacher and Qwen2.5-7B-Instruct as student across three benchmarks (MATH-500, HumanEval+, MT-Bench). Our results reveal that, in a same-family distillation setting against a naive attacker, most output-level defenses are surprisingly ineffective: paraphrasing-based perturbation barely degrades distilled student quality, and data poisoning primarily impairs conversational fluency while leaving task-specific capabilities intact. Only chain-of-thought removal substantially impairs mathematical reasoning (31.4% vs.\ 67.8% baseline), though code generation remains unaffected. These findings demonstrate that the effectiveness of distillation defenses is highly task-dependent and that current output-level approaches are insufficient to broadly prevent knowledge theft.

In today's technology-driven world, web services have opened up new opportunities for blind and visually impaired people to interact independently. Securing interactions with these services is crucial; however, currently deployed authentication mainly concentrate on sighted users, overlooking the needs of the blind and visually impaired community. In this paper, we address this gap by investigating the security and accessibility aspects of these authentication when adopted by blind and visually impaired users. We model web authentication for such users as screen reader assisted authentication and introduce an evaluation framework called AWARE. Using AWARE, we then systematically assessed popular PC and smartphone-based screen readers against different authentication methods, including variants of 2FA and passwordless schemes, to simulate real-world scenarios. We analyzed these screen reader assisted authentication interactions with authentication methods in three settings: using a terminal (PC) with screen readers, a combination of the terminal (PC) and smartphone with screen readers, and smartphones with integrated screen readers. The results of our study underscore weaknesses in all of our observed screen reader assisted scenarios for real-life authentication methods. These weaknesses, encompassing specific accessibility issues caused by imprecise screen reader instructions, highlight vulnerability concerning observed scenarios for both real-world and research literature based attacks, including phishing, concurrency, fatigue, cross-service, and shoulder surfing. Broadly, our AWARE framework can be used by designers as a precursor to user studies which are typically time-consuming and tedious to perform, independently allowing to unfold security and accessibility problems early which designers can address prior to full-fledged user testing of more isolated issues.

Collaborative threat intelligence via federated learning (FL) faces critical risks from quantum computing, which can compromise classical encryption methods. This study proposes a quantum-secure FL framework using post-quantum cryptography (PQC) to protect cross-organizational data sharing. We expose vulnerabilities in traditional FL through simulated quantum attacks on RSA encrypted gradients and introduce a hybrid architecture integrating NIST-standardized algorithms CRYSTALS-Kyber for key exchange and CRYSTALS-Dilithium for authentication. Testing on APT attack datasets demonstrated 97.6% threat detection accuracy with minimal latency overhead (18.7%), validating real-world viability. A healthcare consortium case study confirmed secure ransomware indicator sharing without breaching privacy regulations. The work highlights the urgency of quantum ready defenses and provides technical guidelines for deploying PQC in FL systems, alongside policy recommendations for standardizing quantum resilience in threat-sharing networks.

Trust management is a critical research pillar in Vehicular Ad Hoc Networks (VANETs), where the reliability of shared data depends entirely on driver integrity. In these networks, a driver's reputation is dynamically constructed based on the veracity of their recent message history: consistent reliability builds trust, while frequent misinformation leads to exclusion. This study analyses driver announcement characteristics by modelling behavioural transitions-specifically the frequency and nature of shifts between "good" and "bad" states. To facilitate this analysis, three distinct Markov chain-based behavioural models are evaluated with varying degrees of granularity: a 4-state model, a 7-state model, and a high-resolution 11-state model. By simulating announcement and reporting patterns, each model's ability to reflect nuanced behavioural shifts is assessed. Our results confirm that increasing the number of trust states significantly enhances the system's ability to capture complex, dynamic driver behaviours, providing a more robust framework for security in VANETs.

This Systematization of Knowledge (SoK) provides a comprehensive historical analysis of Maximal Extractable Value (MEV) in blockchain systems, tracing its conceptual evolution through three distinct eras. We organize the fragmented literature on MEV into a unified chronological framework, beginning with Era~I (August 2014 - August 2020), which introduced Miner Extractable Value from pmcgoohan's seminal Reddit warning through the “Dark Forest” recognition, covering Proof-of-Work systems with public mempools and Priority Gas Auctions. Era~II (August 2020 - April 2024) marks the generalization to Maximal Extractable Value, encompassing formal taxonomies, Realized Extractable Value, Proposer-Builder Separation, the Ethereum Merge, MEV-Boost, and the integration of non-atomic and CEX-DEX arbitrage. Era~III (April 2024, present) addresses the frontier of Cross-Chain MEV, beginning with early studies on Layer-2 ecosystems, where value extraction spans multiple blockchains, rollups, bridges, and sequencers. We present a conceptual taxonomy distinguishing potential from realized extractable value, and single-domain from cross-domain phenomena. Our systematization identifies mitigations that emerged in response to each era, highlights measurement challenges, and proposes a research agenda for standardized metrics, detection benchmarks, and cross-chain infrastructure design.

Certified deletion ensures that encrypted data can be irreversibly deleted, preventing future recovery even if decryption keys are later exposed. Although existing works have achieved certified deletion across various cryptographic primitives, they rely on central authorities, leading to inherent escrow vulnerabilities. This raises the question of whether certified deletion can be achieved in decentralized frameworks such as Registered Attribute-Based Encryption (RABE) that combines fine-grained access control with user-controlled key registration. This paper presents the first RABE schemes supporting certified deletion and certified everlasting security. Specifically, we obtain the following: - We first design a privately verifiable RABE with Certified Deletion (RABE-CD) scheme by combining our newly proposed shadow registered ABE (Shad-RABE) with one-time symmetric key encryption with certified deletion. - We then construct a publicly verifiable RABE-CD scheme using Shad-RABE, witness encryption, and one-shot signatures, allowing any party to validate deletion certificates without accessing secret keys. - We also extend to privately verifiable RABE with Certified Everlasting Deletion (RABE-CED) scheme, integrating quantum-secure RABE with the certified everlasting lemma. Once a certificate is produced, message privacy becomes information-theoretic even against unbounded adversaries. -We finally realize a publicly verifiable RABE-CED scheme by employing digital signatures for the BB84 states, allowing universal verification while ensuring that deletion irreversibly destroys information relevant to decryption.

Proof-of-Work (PoW) is a fundamental method in decentralized digital networks for establishing consensus on a shared ledger. By requiring network participants to solve a mathematical puzzle, PoW maintains network integrity. However, PoW has raised environmental concerns due to its significant energy consumption. This paper introduces Proof-of-Encryption-Work (PoEW), a novel PoW consensus mechanism that repurposes computational power to address the challenge of encryption-based data compression. PoEW uses an exhaustive key search as the PoW puzzle. Given a lengthy plaintext and a fixed ciphertext, the corresponding key is derived. Since the plain-text is much longer than both the key and the ciphertext, this process compresses the plaintext to the key. This data compression is computationally intensive, while decompression is straightforward.

Membership inference attacks (MIAs) have become the standard tool for evaluating privacy leakage in machine learning (ML). Among them, the Likelihood-Ratio Attack (LiRA) is widely regarded as the state of the art when sufficient shadow models are available. However, prior evaluations have often overstated the effectiveness of LiRA by attacking models overconfident on their training samples, calibrating thresholds on target data, assuming balanced membership priors, and/or overlooking attack reproducibility. We re-evaluate LiRA under a realistic protocol that (i) trains models using anti-overfitting (AOF) and transfer learning (TL), when applicable, to reduce overconfidence as in production models; (ii) calibrates decision thresholds using shadow models and data rather than target data; (iii) measures positive predictive value (PPV, or precision) under shadow-based thresholds and skewed membership priors (pi <= 10%); and (iv) quantifies per-sample membership reproducibility across different seeds and training variations. We find that AOF significantly weakens LiRA, while TL further reduces attack effectiveness while improving model accuracy. Under shadow-based thresholds and skewed priors, LiRA's PPV often drops substantially, especially under AOF or AOF+TL. We also find that thresholded vulnerable sets at extremely low FPR show poor reproducibility across runs, while likelihood-ratio rankings are more stable. These results suggest that LiRA, and likely weaker MIAs, are less effective than previously suggested under realistic conditions, and that reliable privacy auditing requires evaluation protocols that reflect practical training practices, feasible attacker assumptions, and reproducibility considerations. Code is available at https://github.com/najeebjebreel/lira_analysis.

Advanced Persistent Threats (APTs) evolve through multiple stages, each exhibiting distinct temporal and structural behaviors. Accurate stage estimation is critical for enabling adaptive cyber defense. This paper presents StageFinder, a temporal-graph learning framework for multi-stage attack progression inference from fused host and network provenance data. Provenance graphs are encoded using a graph neural network to capture structural dependencies among processes, files, and connections, while a long short-term memory (LSTM) model learns temporal dynamics to estimate stage probabilities aligned with the MITRE ATT&CK framework. The model is pretrained on the DARPA OpTC dataset and fine-tuned on labeled DARPA Transparent Computing data. Experimental results demonstrate that StageFinder achieves a macro F1-score of 0.96 and reduces prediction volatility by 31% compared to state-of-the-art baselines (Cyberian, NetGuardian). These results highlight the effectiveness of fused provenance-temporal learning for accurate and stable APT stage inference.

Artificial Intelligence (AI) agents have evolved from passive predictive tools into active entities capable of autonomous decision-making and environmental interaction, driven by the reasoning capabilities of Large Language Models (LLMs). However, this evolution has introduced critical security vulnerabilities that existing frameworks fail to address. The Hierarchical Autonomy Evolution (HAE) framework organizes agent security into three tiers: Cognitive Autonomy (L1) targets internal reasoning integrity; Execution Autonomy (L2) covers tool-mediated environmental interaction; Collective Autonomy (L3) addresses systemic risks in multi-agent ecosystems. We present a taxonomy of threats spanning cognitive manipulation, physical environment disruption, and multi-agent systemic failures, and evaluate existing defenses while identifying key research gaps. The findings aim to guide the development of multilayered, autonomy-aware defense architectures for trustworthy AI agent systems.

The Model Context Protocol (MCP) is an open and standardized interface that enables large language models (LLMs) to interact with external tools and services, and is increasingly adopted by AI agents. However, the security of MCP-based systems remains largely unexplored.In this work, we conduct a large-scale security analysis of MCP servers integrated within MCP clients. We show that treating MCP servers as trusted entities without authenticating the caller identity is fundamentally insecure. Since MCP servers often cannot distinguish who is invoking a request, a single authorization decision may implicitly grant access to multiple, potentially untrusted callers.Our empirical study reveals that most MCP servers rely on persistent authorization states, allowing tool invocations after an initial authorization without re-authentication, regardless of the caller. In addition, many MCP servers fail to enforce authentication at the per-tool level, enabling unauthorized access to sensitive operations.These findings demonstrate that one-time authorization and server-level trust significantly expand the attack surface of MCP-based systems, highlighting the need for explicit caller authentication and fine-grained authorization mechanisms.

Cloud-based infrastructure has become the dominant platform for deploying large models, particularly large language models (LLMs). Fine-tuning and inference are increasingly delegated to cloud providers for simplified deployment and access to proprietary models, yet this creates a fundamental trust gap. Although cryptographic and TEE-based verification approaches exist, prohibitive proving costs and limited TEE memory prevent them from scaling to modern LLMs, leaving clients unable to practically audit these processes. This lack of transparency creates concrete security risks that can silently compromise service integrity. We present AFTUNE, an auditable and verifiable framework that ensures the computational integrity of cloud-based fine-tuning and inference. AFTUNE incorporates a lightweight recording and spot-check mechanism that produces verifiable traces of execution. These traces enable clients to later audit whether the fine-tuning and inference processes followed the agreed configurations, by verifying sampled execution blocks inside a TEE, each covering only a small portion of the model and the execution trace. Our evaluation shows that AFTUNE adds modest overhead and makes auditing practical for clients.

Large Language Models (LLMs) are increasingly integrated into safety-critical workflows, yet existing security analyses remain fragmented and often isolate model behavior from the broader system context. This work introduces a goal-driven risk assessment framework for LLM-powered systems that combines system modeling with Attack-Defense Trees (ADTrees) and Common Vulnerability Scoring System (CVSS)-based exploitability scoring to support structured, comparable analysis. We demonstrate the framework through a healthcare case study, modeling multi-step attack paths targeting intervention in medical procedures, leakage of electronic health record (EHR) data, and disruption of service availability. Our analysis indicates that threats spanning (i) conventional cyber, (ii) adversarial ML, and (iii) conversational attacks that manipulate prompts or context often consolidate into a small number of dominant paths and shared system choke points, enabling targeted defenses to yield meaningful reductions in path exploitability. By systematically comparing defense portfolios, we align these risks with established vulnerability management practices and provide a domain-agnostic workflow applicable to other LLM-enabled critical systems.

Backdoor mechanisms have traditionally been studied as security threats that compromise the integrity of machine learning models. However, the same mechanism – the conditional activation of specific behaviors through input triggers – can also serve as a controllable and auditable interface for trustworthy model behavior. In this work, we present Backdoor4Good (B4G), a unified benchmark and framework for beneficial backdoor applications in large language models (LLMs). Unlike conventional backdoor studies focused on attacks and defenses, B4G repurposes backdoor conditioning for Beneficial Tasks that enhance safety, controllability, and accountability. It formalizes beneficial backdoor learning under a triplet formulation $(T, A, U)$, representing the Trigger, Activation mechanism, and Utility function, and implements a benchmark covering four trust-centric applications. Through extensive experiments across Llama3.1-8B, Gemma-2-9B, Qwen2.5-7B, and Llama2-13B, we show that beneficial backdoors can achieve high controllability, tamper-resistance, and stealthiness while preserving clean-task performance. Our findings demonstrate new insights that backdoors need not be inherently malicious; when properly designed, they can serve as modular, interpretable, and beneficial building blocks for trustworthy AI systems. Our code and datasets are available at https://github.com/bboylyg/BackdoorLLM/B4G.