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99 MATCHING RECORDSMETADATA ONLY / SOURCE ATTRIBUTION PRESERVED
arXiv:2610.07639v1 Announce Type: new Abstract: Coding agent harnesses mediate tool use and authorize actions, yet their security mechanisms and runtime effects remain incompletely characterized. We present HarnessSecurity, the first systematic empirical study and benchmark of open- and closed-source coding agent harnesses. First, we derive a ten-mechanism taxonomy and then assess 400 harness-mech
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08098v1 Announce Type: new Abstract: AI agents increasingly rely on modular third-party "skills" that are dynamically selected by skill routers to execute complex tasks. While recent studies highlight the threat of prompt injections embedded in these skills, existing evaluations often assume settings where the malicious skill is already selected for execution. We show that this assumpti
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08668v1 Announce Type: new Abstract: Behavioral watermarking embeds an owner identifier in an LLM agent's high-level action choices, giving provenance without touching output tokens. Prior agent watermarks break in two ways. First, all three prior schemes bind the watermark to the exact action symbol, so renaming a tool desynchronizes decoding even when the observation is untouched; in
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2608.03134v2 Announce Type: replace Abstract: Detecting source code vulnerabilities is increasingly difficult as modern security flaws are rooted in complex causal dependencies between execution flows, control conditions, and program states. Despite recent advances in Large Language Models (LLMs) and multi-agent frameworks, existing approaches primarily address superficial similarities betwe
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2605.17061v2 Announce Type: replace Abstract: FIPS 203, 204 and 205 closed the algorithmic gap in post-quantum cryptography (PQC); the production gap -- hybrid combiners, conformance evidence, migration tooling, stateful signing, protocol helpers -- remains open. A methodology that scores it must not choose its own dimensions, so we anchor nine to external requirements (CNSA 2.0, CMVP, the T
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07820v1 Announce Type: new Abstract: Efficient post-quantum cryptography on resource-constrained Internet-of-Things (IoT) devices requires implementations that exploit the target processor architecture while resisting practical implementation attacks. This paper presents an ISA-accelerated and implementation-hardened realization of Ring Binary Learning with Errors (RBLWE) encryption on
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08090v1 Announce Type: new Abstract: IPv6 extension headers (EHs), such as fragmentation, segment routing, and in-situ telemetry, are operationally important yetwidely dropped in transit, and characterising their behaviour from packet captures is a recurring measurement problem. We ask whetheran explainable miner can recover human-readable rules of EH behaviour, and we contribute two re
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07251v1 Announce Type: cross Abstract: GHZ-based conference key agreement distributes a shared key to $N$ parties in one network use, whereas pairwise BB84 needs $N-1$. The survival of that advantage has been studied against party number, loss, and topology, but almost always with the channel fixed at depolarizing. Simulating both protocols exactly for $N = 3, 4, 5$ under dephasing, amp
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07771v1 Announce Type: new Abstract: Functional backdoor recovery finds any trigger whose attack success rate is at least a given threshold rather than to recover the planted trigger. We study the minimum number of queries required for this task under label feedback which returns the predicted class label. We construct two finite families of victim models that have exactly the same atta
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08262v1 Announce Type: new Abstract: Can authentication make memory, rather than computational hardness, the attacker's bottleneck? Contextual Chain is a lightweight continuity protocol for intermittently connected devices that share evolving physical or operational context. An honest device follows one realized history, updating a compact accumulator and fixed hash-based readiness lane
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2606.15020v2 Announce Type: replace Abstract: Document-to-LLM applications typically read uploaded PDFs by first translating them into text through an extraction layer whose output is not ordinarily exposed to users. We show that this layer enables split-view PDFs: one document can have two semantic views before model reasoning. By mining specification-permitted or implementation-tolerated r
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07124v1 Announce Type: cross Abstract: We prove optimal query bounds for recovering a single qubit from black-hole radiation in the Haar random oracle model, when the remaining black hole contains at most one sixteenth of the system's qubits. Recovery with any constant Haar-averaged advantage over trivial decoding requires queries proportional to the Hilbert-space dimension of the remai
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.06898v1 Announce Type: new Abstract: As GUI-based mobile agents rapidly progress, rigorous safety evaluation of their autonomous decision-making in realistic app interfaces becomes increasingly critical. Existing benchmarks mainly focus on execution-level anomalies using task success or hijack rates, but fail to capture the in-task goal deviation risk in multi-candidate selection tasks,
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08097v1 Announce Type: new Abstract: Mathematical problem solving often requires deterministic computational steps that agents delegate to tools and implicitly trust. Yet tools can fail silently, returning plausible but incorrect results. How well can agents detect and correct corrupted tool call outputs? We study this through a controlled corruption framework where a hidden interceptor
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08178v1 Announce Type: cross Abstract: Existing latent-based watermarking methods for diffusion models have overestimated their robustness to image distortions, including geometric transformations such as rotation, scaling, and translation (RST). Moreover, this paradigm of watermarking approaches may suffer from inherent limitations arising from the domain in which the watermark is embe
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08406v1 Announce Type: new Abstract: Dynamic Application Security Testing (DAST) scanners achieve high recall but also produce a large number of false positives, resulting in substantial manual triage costs. Large Language Models (LLMs), when used for independent detection, achieve extremely high recall (95.4%-100%) but also exhibit prohibitively high false positive rates (49.6%-85.0%),
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07351v1 Announce Type: new Abstract: Extremely Lossy Functions (ELFs) are a standard model primitive that captures many useful properties of random oracles (Zhandry, Crypto 2016). While there are many variations of ELFs with additional properties, every construction (excluding obfuscation) has followed essentially the same template of bootstrapping from a sequence of ELFs secure only ag
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07691v1 Announce Type: new Abstract: Detecting microarchitectural attacks is critical given their proliferation in recent years. Many of these attacks exhibit intrinsic behaviors essential to the nature of their operation, such as creating contention or misspeculation. This study systematically investigates the microarchitectural footprints of hardware-based attacks and shows how they c
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2512.11147v2 Announce Type: replace Abstract: AI agents are increasingly granted autonomous access to sensitive user data and third-party services, making effective permission management a critical security challenge. Existing permission models, however, typically rely on flat permission structures that fail to balance security with usability: fine-grained confirmation induces user fatigue,
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07657v1 Announce Type: cross Abstract: LLM-based multi-agent systems (MAS) engage tools, share memory, and delegate tasks, often encountering adversarial content. Current defenses for MAS are typically evaluated in isolation, focusing on one attack type at a time, which can lead to costly and hard-to-audit outcomes. This study organizes defenses into five principles, implementing them a
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07866v1 Announce Type: new Abstract: Multi-framework Governance, Risk and Compliance (GRC) platforms increasingly automate the link between an organisation's self-assessment answer and the compliance obligations that answer is said to satisfy. Cross-framework control mapping, AI-suggested question correlation, and automatic propagation of answers and evidence across correlated questions
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
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