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arXiv:2610.07125v1 Announce Type: new Abstract: While open-weight models have enjoyed steady progress in capabilities and wide adoption across multiple domains, their safety remains an important concern. One key feature is the ability to refuse or deflect harmful, malicious, or insensitive prompts. In this paper, we expose safety vulnerabilities across six common open-weight LLMs of various sizes
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08240v1 Announce Type: cross Abstract: Large Language Models (LLMs) are widely used to generate code. Although their functional plausibility keeps improving, the generated code often contains security vulnerabilities. The functionality-security gap captures code that passes functional tests but fails security tests. A recent longitudinal study of three model families concluded that LLMs
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08137v1 Announce Type: new Abstract: AI systems create images and videos with image/video generation models or by writing code and graphics descriptions that are then rendered. These routes can produce similar visible artifacts but expose different representations, intervention points, and provenance evidence. We develop a production-centered framework that compares detection and waterm
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.06475v2 Announce Type: replace-cross Abstract: DRAM continues to limit the performance, energy efficiency, and robustness of modern systems. Many prior works propose DRAM techniques that support in-DRAM computation, improve memory access latency and parallelism, and enhance DRAM maintenance and reliability. However, adopting each new technique requires repeated modifications to the rigi
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07875v1 Announce Type: new Abstract: Collaborative perception (CP) enables connected vehicles to see beyond their own sensors but makes them dependent on messages they cannot independently verify. A compromised collaborator can surgically conceal a single safety-critical object or inject a non-existing one while correctly reporting many others. Existing Bayesian trust mechanisms pool ag
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07323v1 Announce Type: cross Abstract: Security evaluation of learning-based systems requires more than just testing the system against a fixed collection of attacks. It requires adaptive mechanisms that can efficiently discover \textit{sets} of inputs that induce model failure. We introduce ATLAS (Adaptive Trust-Regions for Latent Adversarial Searches), which is a query-based framework
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07531v1 Announce Type: new Abstract: A person who answers an unexpected call claiming to come from a bank has no way to check the claim. Australian text messaging has labelled a message as unverified when the sender identifier is not registered since 1 July 2026, but a voice call still arrives with nothing behind it, and voice cloning has removed the last cue recipients relied on. We pr
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07258v1 Announce Type: new Abstract: Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimately computed results the requester could not derive, and departments can silently compute a same-named key performance indicator (KPI) through conflicting logic. Existing agent-memory systems (e.g., MemGPT, Zep, A-MEM) gate retrieval by
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07276v1 Announce Type: cross Abstract: Deployment-time safety of language models is commonly implemented through runtime guardrails such as input moderation, routing, retrieval verification, and output filtering. Existing deployment frameworks provide increasingly capable mechanisms for these functions, but offer limited guidance on how the safety decisions they produce should be explic
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08174v1 Announce Type: new Abstract: Continuous authentication (CA) strengthens session security by repeatedly verifying the user during device interaction, yet it inherently relies on highly sensitive behavioral traces (e.g., fine-grained touch dynamics) that are often outsourced to cloud/edge services for scalable inference. This raises a fundamental privacy-in-use challenge: protecti
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08464v1 Announce Type: new Abstract: Learned surgical simulators and world models can roll out plausible procedural futures, but they carry no grounded estimate of how often interventional devices actually harm patients. We propose treating population-scale adverse-event surveillance as a distinct belief layer of the surgical digital twin, and we evaluate a federated Bayesian protocol f
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08739v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) are integral to many safety critical systems, yet they remain highly vulnerable to bit-flip attacks (BFAs), where a few memory level perturbations can drastically degrade accuracy. Existing defenses incur significant hardware overhead, depend on retraining, or fail against targeted flips. We propose BARE-AI, a runtime fram
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08590v1 Announce Type: new Abstract: Learning-based semantic communication is vulnerable to adversarial perturbations introduced before semantic encoding or over wireless channels. This paper proposes TwinViT-DeepJSCC, a preventive-corrective semantic image transceiver operating under a fixed channel-use budget. Two Vision Transformer (ViT)-based deep joint source-channel coding (DeepJS
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07476v1 Announce Type: cross Abstract: Frontier AI treaties or agreements on limiting computation require external verification; an external auditor must be able to confirm how much computation actually ran and that parties are adhering to the agreement. Analogue, off-chip measurements such as power draw provide an information channel for verification. It is unknown how well these analo
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.06900v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in binary decompilation to refine the C-like pseudocode produced by traditional rule-based decompilers. While this pseudocode is useful, it is a heuristic and lossy abstraction rather than a faithful copy of the source code. It often contains decompiler errors, especially for aggressively optimized
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.08316v1 Announce Type: new Abstract: Protein Generative Models (PGMs) have revolutionized structural biology by enabling the design of complex 3D protein structures from sequence data. However, this breakthrough introduces a dual-use challenge, exposing high-value PGMs to economic risks like unauthorized model extraction and biosecurity threats such as biohazard synthesis. To mitigate t
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2608.14089v3 Announce Type: replace-cross Abstract: Safety classifiers deployed with large language models often fail for two reasons: their decisions reflect the policy learned during training rather than the deployer's desired policy, and their performance degrades as deployment traffic evolves. We present Regime-Conditional Verification (RCV), a lightweight wrapper that adapts an off-the-
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07298v1 Announce Type: new Abstract: Cyber threat analysis increasingly depends on evidence distributed across vendor advisories, vulnerability databases, and threat intelligence sources. Turning these fragmented observations into timely decisions requires models to connect technical severity with evolving exploitation evidence and available defensive actions. We present POLAR, an LLM-p
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.06966v1 Announce Type: new Abstract: Indirect prompt injection (IPI) hides adversarial instructions in content that large language model (LLM) agents read at runtime. As agents compose heterogeneous capability units, including Tools, MCP servers, and Skills, the carriers of injection multiply, and defenses built to recognize attack patterns fall behind them. We instead shift defense fro
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2603.27224v5 Announce Type: replace-cross Abstract: Memory leaks remain prevalent in real-world C/C++ software. Static analyzers such as CodeQL provide scalable program analysis but frequently miss such bugs because they cannot recognize project-specific custom memory-management functions and lack path-sensitive control-flow modeling. We present MemHint, a neuro-symbolic pipeline that addres
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2610.07976v1 Announce Type: new Abstract: Operator-side network profiles derived from 5G/ORAN traffic carry personal data such as ephemeral subscriber identifiers, slice-level KPIs, and control-plane signalling, and must be anonymised before release to a federated-learning aggregator, threat-intelligence exchange, or ML training pipeline. We study how much re-identification risk remains afte
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
arXiv:2508.19819v3 Announce Type: replace Abstract: Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly strong reconstructions under favorable experimental settings. However, it remains unclear whether such attacks are feasible in modern, performance-optimized systems deployed in practice. In this work, we evalua
First reported Oct 07, 2026 04:00 UTCCatalog metadata updated Oct 08, 2026 03:43 UTCOpen evidence record
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