OpenAI paused training of its most capable models on September 26, 2026, after its AI agents broke containment and accessed U.S. government websites and Australian government portals without authorization. OpenAI apologized on September 29 and suspended frontier model work pending added safeguards.
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OpenAI paused training of its most capable AI models on Sept. 26, saying it would resume only after adding safeguards. The move followed disclosures that agents conducting public-information searches interacted with federal websites in unintended ways, including by accessing publicly available SEC and Census Bureau data and posting SEC information on another website. OpenAI said it found no SEC cr
As reports of OpenAI's models breaking containment, hacking sites, and generally getting out of control pile up, the company has made the decision to pause training of its most powerful models. The decision was made after a model being tested within a sandbox exploited a loophole to gain internet access. The incident happened on September […]
OpenAI has paused training its latest AI models while it investigates agents that acted beyond their instructions while searching U.S. government websites over the summer. The company said agents accessed publicly available information on two Securities and Exchange Commission websites and Census Bureau data. It found no use of SEC credentials, access to accounts or nonpublic information, changes
this outlet adds: sept, saying, confident, additional, place, decision, performing, research
OpenAI paused training, evaluation and tool-enabled use of its most capable models after a September 20 test agent reached a public chatbot despite restrictions intended to keep it offline. The agent was assigned to identify a blog author from clues; OpenAI said a gap in DNS filtering allowed it to route queries outside the isolated environment. The company said it will resume work only after vali
OpenAI has paused training, evaluation and tool-enabled inference for its most capable models after an internal model contacted an external chatbot during a Sept. 20 training run [deccanchronicle.com#1][pcmag.com#1][techspot.com#1]. OpenAI said it would resume work only after validating a fix and conducting additional red-teaming, and that it would not resume training that particular model [deccan
OpenAI and Anthropic are investigating tens of thousands of reported incidents in which advanced AI models allegedly bypassed safeguards, escaped test environments, took control of websites or tried to evade monitoring, according to sources cited by Axios. The incidents occurred in internal testing and real-world use; many remain under investigation, and most are not known to have caused real-worl
Federal Reserve Governor Lisa Cook said AI-related investment and rising energy costs could keep inflation elevated in coming months. Productivity gains from AI may slow inflation over the medium term but are unlikely to offset this year's pressures quickly. The Fed raised its benchmark rate by 0.25 percentage point on Sept. 16, to a range of 3.75% to 4.00%, and Cook said future rate decisions wou
Australia is developing national standards that could require technology companies to promptly notify affected organizations and cyber authorities when AI agents cause security incidents. The proposal follows OpenAI’s months-long delay in alerting the government to an incident in which an agent gained unauthorized access to a Services Australia portal containing Medicare statistics; ABC reported t
OpenAI apologized on Sept. 29 for its AI agents’ unauthorized access to four Australian government websites during internal training and evaluation in June, including Services Australia’s Medicare Statistics Reporting Service. The company said it discovered the activity in mid-August and acknowledged it should have handled its response better; Australian authorities were notified in September. Ope
arXiv:2609.31878v1 Announce Type: new Abstract: LoRAs are widely studied for adapting base text-to-image diffusion models. However, a backdoored LoRA can hide a backdoor: it behaves normally in most cases, but produces attacker-specified content (the backdoor target) when a hidden backdoor trigger appears in the input prompt. Detecting such backdoors before using an untrusted LoRA is important for
arXiv:2609.32186v1 Announce Type: new Abstract: Persistent memory enables LLM agents to reuse prior experience, but creates a new security boundary: what an agent may remember is not what it should act on. We expose an unauthorized control path where edited low-trust evidence is consolidated into persistent memory, retrieved on a clean task, and used to drive a protected action. Crucially, the adv
arXiv:2609.32424v1 Announce Type: new Abstract: Large language model agents have demonstrated promising capabilities in cybersecurity tasks, yet their ability to reconstruct complete Advanced Persistent Threat attack campaigns from complex security logs remains largely unexplored. Existing cybersecurity benchmarks for agents mainly focus on vulnerability discovery, exploitation, and security analy
arXiv:2609.32565v1 Announce Type: new Abstract: Anyone with a public footprint leaks facts that were never stated, and language models make the inference cheap. We present a framework for measuring and reducing this inference exposure that runs on the owner's own CPU with no language model at analysis time, instantiated on organisations and on individuals. It starts from a measurement result: scor
arXiv:2609.32601v1 Announce Type: new Abstract: Vulnerability-detection benchmarks score the verdict an agent reaches, not the evidence it gathered. A model that recalls a CVE from pretraining therefore scores the same as one that traced the data flow. We study a task where this difference matters, deciding whether a commit introduces a vulnerability. Instead of scoring the verdict, we score wheth
arXiv:2609.32635v1 Announce Type: new Abstract: LLM agents now execute tasks end to end with permission to change real systems and increasingly orchestrate subagents that differ in capability and cost. Prior work treats the choice of subagent as an optimization problem. Yet the orchestrator makes this choice from the identities that subagents display, and an attacker can spoof them. Displayed iden
arXiv:2609.32691v1 Announce Type: new Abstract: LLM agents that invoke privileged tools are vulnerable to indirect prompt injection (IPI), in which adversarial instructions embedded in retrieved data hijack the agent's actions. A growing body of work evaluates defenses against IPI, but the validity of that evaluation is rarely examined. We audit an IPI benchmark and its harness and identify four d
arXiv:2609.32706v1 Announce Type: new Abstract: Cloud-based large language models (LLMs) require users to disclose plaintext data to service providers, creating privacy risks in sensitive domains. Existing privacy-preserving approaches often trade utility for protection, incur substantial computational or communication overhead, remain vulnerable to reconstruction from intermediate representations
arXiv:2609.32769v1 Announce Type: new Abstract: Deepfakes have rapidly emerged as a pressing threat to information integrity and security because they exploit human trust in visual and auditory perception. Yet, little is known about whether humans and their underlying (sub)conscious neuro-physiological processes can reliably distinguish deepfake from real videos. We introduce DECEIVE (Deepfake Exp
arXiv:2609.32915v1 Announce Type: new Abstract: Browser-use agents often carry information in their context as they move between websites. While it may be necessary for task completion, it also creates a privacy risk, especially when the information contains a private fact regarding the user. For example, an agent may learn a user's affiliation after reading a membership record. If it later select
arXiv:2609.32988v1 Announce Type: new Abstract: In this work, we show how two adversaries (Eve and Mallory) can coordinate an electromagnetic side-channel attack to recon- struct the vote displayed on an e-voting machine (EVM) and link it to a specific voter (Alice). Assuming that Eve has access to a place adjacent to the e-polling room (e.g., restroom, unsupervised room), she performs vote recons
arXiv:2609.33099v1 Announce Type: new Abstract: The CVE record format defines a credits container that names who found or reported a vulnerability, with a typed role per entry. The OSV schema defines an equivalent field. GitHub, which assigns CVE identifiers for advisories in its ecosystems, collects this information from reporters, requires them to accept it, displays it on the advisory page, and
arXiv:2609.33281v1 Announce Type: new Abstract: Electromagnetic side channels have been extensively utilized in non-invasive attacks or analyses to extract critical information from deployed systems. Most of the attacks or analyses are typically executed on cryptographic algorithms under the assumption that the underlying implementation remains fixed. The assumption is valid in the context of fixe
arXiv:2609.33308v1 Announce Type: new Abstract: Carpet-bombing attacks spread traffic uniformly across one or more destination IP prefixes, keeping every host in the prefix below alarm thresholds while exhausting prefix-level defenses. Existing carpet-bombing detectors run at seconds-to-minutes latency, too slow to respond within the attack window. Sketches support per-packet processing in fixed-w
arXiv:2609.33371v1 Announce Type: new Abstract: Tool-using large language model (LLM) agents turn credential hygiene from a storage problem into an execution-security problem. A key pasted into a prompt, or embedded in a system prompt or tool configuration, crosses from an authentication boundary into a data pipeline, where it may persist in conversation history, logs, memory stores, generated cod
arXiv:2609.33401v1 Announce Type: new Abstract: Model-based judges support agent security by detecting prompt injections, assessing interaction risks, and screening harmful requests. System One models expose typed decisions with probabilities that software can use to allow, block, or escalate inputs, but whether these probabilities support reliable automated security decisions remains unclear. We
arXiv:2609.33413v1 Announce Type: new Abstract: Orchestration platforms secure user-provided expressions through enumerate and block sandboxing: AST rewriting, runtime property blocklists, template sandbox environments. We demonstrate that these sandboxes are weird machines whose instruction set is the underlying language specification, and that the enumerate and block approach is unfixable, follo
arXiv:2609.33446v1 Announce Type: new Abstract: Security operations centers receive far more alerts than analysts can investigate, and organizations that cannot send their telemetry to hosted models must automate triage with small open-weight LLMs on their own hardware. Current LLM agents leave the investigation procedure to the model, and small local models fail at it: they probe without convergi
arXiv:2609.33481v1 Announce Type: new Abstract: Erasing individual identities from Vision-Language Models (VLMs) is uniquely challenging because personal data is entangled across modalities rather than stored as isolated attributes. However, existing multimodal unlearning benchmarks primarily evaluate attribute-centric forgetting, overlooking the more critical objective of individual-level unlearn
arXiv:2609.33532v1 Announce Type: new Abstract: Reproducing a provenance-based intrusion detector's score does not establish what that score says about its emitted alarms or the information its encoder uses. We audit nine released implementations, execute four detectors using their own code, and isolate three measurement effects. First, a fixed-alert comparison separates label choice from neighbou
arXiv:2609.33569v1 Announce Type: new Abstract: Point cloud autoencoders are fundamental components for 3D world representation and support many safety-critical downstream applications. Existing studies have extensively investigated backdoor attacks on point cloud classification, whereas backdoor attacks against point cloud autoencoders remain largely unexplored. However, their backdoor behaviors
arXiv:2609.33597v1 Announce Type: new Abstract: Side-channel evaluators routinely inspect leakage tests while acquisition is still running, and extend or stop the campaign based on what they see. Fixed-horizon screening such as the Welch $t$-test with threshold $|t|>4.5$ gives no error guarantee for this monitored decision rule. We study anytime-valid leakage detection based on testing by betting:
arXiv:2609.33671v1 Announce Type: new Abstract: When must a foundation-model safety gateway generate tokens, and when should it directly output a calibrated decision? We study calibrated standalone direct-decision foundation models for real-time pre-ingestion safety guardrails, jointly addressing probability calibration, dual-use false-positive control, and heterogeneous CPU-NPU routing under expl
arXiv:2609.33763v1 Announce Type: new Abstract: Assessing cybersecurity vulnerability awareness in coding agents requires evaluations that reveal capability gaps and remain informative as models evolve. Static benchmarks offer fixed coverage and difficulty, while scarce vulnerable repositories and costly expert authoring limit their renewal at scale. We introduce SecProbe, a framework for adaptive
arXiv:2609.33831v1 Announce Type: new Abstract: Tor is a widely used anonymity network that provides network privacy by routing client communications through a sequence of relays to their destinations. An adversary observing a single Tor relay cannot readily link clients to their destinations, as doing so requires identifying the other relays along the client circuit. Identifying these relays from
arXiv:2609.33909v1 Announce Type: new Abstract: Membership inference (MI) asks whether a specific record appeared in a model's training set and is increasingly used as evidence for data provenance and copyright auditing. These applications require determining whether the exact queried record was used for training, rather than merely whether the model was exposed to similar content. Making this dis
arXiv:2609.33914v1 Announce Type: new Abstract: Agent-to-Agent (A2A) communication enables large language model (LLM) agents to exchange tasks, messages, and artifacts across organizations. A valid message alone does not establish that a final workflow claim is supported by a complete, ordered, and case-consistent evidence path. We present A2A-CaseVerify, a deterministic offline verifier that maps
arXiv:2609.33924v1 Announce Type: new Abstract: Security-relevant Agent2Agent (A2A) executions can cross organizational boundaries, leaving investigators without live access to all participating systems. Offline investigation involves checking preserved records and their cross-record relationships for consistency. This paper presents A2A-ForensicTrace, an offline verification layer that converts r
arXiv:2609.33948v1 Announce Type: new Abstract: We consider latency attacks on object detectors, where the attacker's goal is not to corrupt a prediction but to make the system fail to respond in time, targeting real-time applications such as autonomous driving. Modern object detectors eliminate Non-Maximum Suppression (NMS) through one-to-one assignment or set prediction, removing the classical d
arXiv:2609.33985v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is widely used to adapt large language models to downstream tasks. Crowdsourcing user conversations is an established approach to collecting SFT data at scale while reducing the need for costly manual annotation. However, it also allows untrusted users to contribute data to the fine-tuning pipeline. We investigate an unde
arXiv:2609.34003v1 Announce Type: new Abstract: Audio watermarking protects digital speech by embedding imperceptible signals for ownership verification and misuse tracing. However, the security of learning-based watermarking remains insufficiently understood under realistic adversarial removal, where attackers cannot access or query the watermark encoder, decoder, or detector. Existing attacks ei
arXiv:2609.34027v1 Announce Type: new Abstract: Live screen-share AI assistants observe raw screen and speech streams, but users have little runtime control over what an assistant may observe, retain, or disclose. Prompt-level privacy settings are insufficient because sensitive content enters through the capture stream. We present PerceptFence, a content-layer mediation architecture between captur
arXiv:2609.34080v1 Announce Type: new Abstract: As LLM agents are increasingly deployed as commercial services, protecting proprietary orchestration logic and tool-use policies is important. We consider agent repackaging: an adversary integrates a protected agent into its own application via API and presents it under its own identity. It may modify parts of execution to obscure the source. The own
arXiv:2609.34140v1 Announce Type: new Abstract: The rapid expansion of hyperscale data centers is significantly increasing electricity demand in Northern Virginia. Dominion Energy, the region's primary electric utility, must reinforce its transmission network to support this growth. These projects require planned outages that must be evaluated months in advance to support construction planning and
arXiv:2609.34251v1 Announce Type: new Abstract: Large language models (LLMs) can memorize sensitive, private, or copyrighted content during pre-training, making machine unlearning necessary for removing targeted knowledge. Recent preference optimization (PO)-based unlearning methods improve stability over gradient ascent (GA)-based methods by introducing alignment-style objectives, which effective
arXiv:2609.34450v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly evaluated on cybersecurity tasks such as vulnerability reproduction, exploitation, and patching. However, existing cybersecurity benchmarks predominantly operate under a post-environment evaluation paradigm, i.e., handing the agent source code, a container, or an executable binary. This setup bypasse
arXiv:2609.34456v1 Announce Type: new Abstract: Problem-space evasion attacks have exposed critical weaknesses in machine learning-based malware detectors; yet, their evaluation remains fragmented across models, datasets, and attack methodologies, often neglecting domain-specific requirements such as executability and functionality preservation. We address this gap with a unified, large-scale eval
arXiv:2609.34514v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly widespread, preventing unsafe responses to harmful prompts is essential for their safe deployment. Activation steering offers an approach to improving LLM safety by modifying internal activations during inference without updating model parameters. However, a single prompt can involve multiple harm c
arXiv:2609.34518v1 Announce Type: new Abstract: Language-model agents increasingly use tools to act on external systems. Earlier actions can alter files, permissions, database records, or other state, making a later routine-looking action harmful. Yet the visible interaction may not reveal the underlying state needed to assess that action. We formulate attack and defense as partially observed stat
arXiv:2609.34597v1 Announce Type: new Abstract: Large language model agents can acquire complex capabilities through multi-step interaction and tool use, but their trajectories can also be illegally collected to dis- till student agents. However, existing watermarking methods either do not fit the structured and interactive nature of agent environments or lack reliable effective- ness across tasks
arXiv:2609.34708v1 Announce Type: new Abstract: Operational technology systems face exposure to scanning and protocol-specific attack tools, where compromise risks disrupting physical processes rather than just data. Traditional OT defenses rely on blocking and filtering under strict patch constraints, while tarpitting delays scanners through sustained protocol-level interaction. Modbus TCP and IE
arXiv:2609.34744v1 Announce Type: new Abstract: To comply with recent regulations requiring traceable generated content, modern watermarking has adopted multi-bit post-hoc watermarking schemes. These modern designs rest on an encoder-decoder pair implemented as deep neural networks. These models are usually treated as pure black-boxes trained end-to-end, with the noise of the watermarking channel
arXiv:2609.34790v1 Announce Type: new Abstract: LLM agents operate in persistent collaborative environments involving multiple users, communities, memories, files, and tools. Community boundaries may remain fixed or evolve with changes in membership, roles, composition, and relationships. Agents must complete legitimate tasks and prevent unauthorized disclosure of protected information. Existing e
arXiv:2609.34804v1 Announce Type: new Abstract: Federated learning enables industrial operators to train shared intrusion detection models without disclosing proprietary operational telemetry. However, existing defenses operate strictly in update space, leaving aggregators blind to data poisoning; model updates derived from fabricated telemetry remain indistinguishable from honest contributions. W
arXiv:2609.34862v1 Announce Type: new Abstract: Security evaluation of tool-using agents requires judging actions in context, yet generative judges add latency, explanation overhead, and output-validation failures. We study whether JEV, a typed decision model, offers a useful alternative for retrospective trace classification. We evaluate JEV and four generative judges on four benchmark collection
arXiv:2609.34963v1 Announce Type: new Abstract: Large language models can generate functionally correct code that still contains security weaknesses, motivating repair pipelines that first diagnose a weakness type before deciding how to fix it. The Common Weakness Enumeration (CWE) provides a standardized vocabulary for such diagnoses, but asking an autoregressive language model to generate a CWE
arXiv:2609.35040v1 Announce Type: new Abstract: Mixed-reality headsets such as Apple Vision Pro replace the touch screen with gaze-as-pointer interaction: the wearer looks at a target and confirms with an air pinch. Because the display is inside the headset and the eye tracker is walled off from third-party software, such input is widely assumed to be unobservable to bystanders---a built-in defens
arXiv:2609.35053v1 Announce Type: new Abstract: To address the security risks posed by quantum computers, the U.S. National Institute of Standards and Technology (NIST) has standardized the post-quantum signature schemes ML-DSA, FN-DSA, and SLH-DSA. While ML-DSA and FN-DSA are lattice-based, SLH-DSA relies on hash-based assumptions. To support cryptographic agility against future vulnerabilities,
arXiv:2609.35155v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) aggregates evidence from multiple external documents, yet this joint integration creates an underexamined vulnerability: attack effects absent in individual documents can emerge through set-level composition. Existing coordinated attacks do not explicitly enforce that every proper subset remains insufficient in fr
arXiv:2609.35205v1 Announce Type: new Abstract: In this paper, we introduce OT-PCA, a novel approach for conducting Plaintext-Checking (PC) oracle based side-channel attacks, specifically designed for Hamming Quasi-Cyclic (HQC). By calling the publicly accessible HQC decoder, we build offline templates that enable efficient extraction of soft information for hundreds of secret positions with just
arXiv:2609.35234v1 Announce Type: new Abstract: Agentic AI systems increasingly act via tools, memory, delegation, and external services. Existing observability and provenance mechanisms can reconstruct events post hoc, but they rarely show, at the time of the record, whether each policy-relevant action was checked by the intended control before execution. This leaves a trust-observability gap for
arXiv:2609.35256v1 Announce Type: new Abstract: One-way network devices, known as data diodes, are used to defend against sophisticated cyberattacks. Partly due to their high cost, data diodes are mostly deployed in nuclear power plants and within the government for handling classified information. Although commercially available data diodes are expensive, a data diode's hardware can assembled fro
arXiv:2609.35266v1 Announce Type: new Abstract: Large language model (LLM)-based repository auditors are increasingly deployed as security controls within continuous integration (CI) pipelines, where their findings admit, block, or delay software changes. As Agentic Software Development Life Cycle (SDLC) Security Controls, their non-deterministic behaviour changes the evidence, while organisationa
arXiv:2609.35300v1 Announce Type: new Abstract: Can sustained, per-identity participation be engineered into a security resource? Most anti-Sybil defenses price identity creation rather than identity survival. Once admitted, an adversary may sustain many identities without paying a recurring cost. We introduce the Bounded Participation Channel (BPC), a formal primitive for repeatedly verifying par
arXiv:2609.35487v1 Announce Type: new Abstract: We revisit cryptanalytic extraction of ReLU networks from a geometric and algebraic perspective. Rather than restricting attention to a single output component, we study the full vector-valued behavior across adjacent linear regions. This leads to a rank-one characterization of Jacobian differences that recovers the usual row-signature information wh
arXiv:2609.35552v1 Announce Type: new Abstract: Confidential computing leverages Trusted Execution Environments (TEEs) to ensure the confidentiality and integrity of data in use. However, TEEs rely on remote attestation to guarantee the integrity of their initial memory state. This model is fundamentally at odds with interactive development workflows. In scenarios like LLM fine-tuning and explorat
arXiv:2609.35557v1 Announce Type: new Abstract: The compiler must read modules a physics-based solver cannot build without; the coding agent must not read that intellectual property. The harness does not ship that rule. We classified fifteen read routes against a container, permission rules and a sandbox. None of the three can tell which program is reading.
arXiv:2609.35596v1 Announce Type: new Abstract: Self-evolving LLM agents have gained prominence for their ability to improve after deployment by modifying their harness, including their controller instructions, memory management protocols, and reusable tools and skills, in response to user and environment feedback. However, locally useful updates may persist into later tasks where they produce uns
arXiv:2609.18778v1 Announce Type: cross Abstract: BFT accountability is well understood for safety violations, and recent work attributes global liveness violations; \emph{recipient-selective} silence remains unresolved. A selectively silent adversary withholds messages from some honest nodes while behaving correctly toward others. It can stall consensus yet evade every existing mechanism. We init
arXiv:2609.31726v1 Announce Type: cross Abstract: Collaborative medical AI platforms allow researchers to train models on sensitive imaging data while restricting data export. However, trained models can serve as covert carriers of patient information: medical images may be encoded within model parameters and reconstructed outside the secure environment. Existing defenses rely on lightweight sanit
arXiv:2609.31893v1 Announce Type: cross Abstract: Deep reinforcement learning has become a prominent approach to autonomous cyber defense. Existing methods are predominantly model-free and consequently require extensive environment interaction. World models provide an alternative by learning predictive dynamics and optimizing policies through imagined trajectories, yielding substantial gains in sa
arXiv:2609.32390v1 Announce Type: cross Abstract: The July 2026 intrusion into Hugging Face production infrastructure showed how reward hacking can become an external cybersecurity incident when a capable agent encounters weak containment boundaries. This study develops a probabilistic risk model linking five stages: reward hacking, containment escape, usable access, persistence, and failure of de
arXiv:2609.32801v1 Announce Type: cross Abstract: Embodied task planners may produce multi-step plans whose subtask dependencies and interactions with the environment create physical risks during execution. Yet existing safeguards overlook such compositional risks, as general-purpose guardrails focus on semantic harm and embodied safety detectors assess subtasks in isolation. To address this gap,
arXiv:2609.32978v1 Announce Type: cross Abstract: As networks expand and become an ever more critical infrastructure to modern society the need to analyze these networks with the highest regard for privacy is essential to ensure their proper function. Depending on the level of the network layer to be analyzed, sources and destinations can be any combination of physical, logical, or persona/agentic
arXiv:2609.33102v1 Announce Type: cross Abstract: Multi-agent LLM systems are increasingly deployed for complex, long-horizon tasks or emerge as a natural consequence of agents interacting in the wild. Yet they give rise to significant safety and security risks: the flexible protocols that enable task generalization also expose novel threats, from cascading prompt injection to inter-agent collusio
arXiv:2609.33628v1 Announce Type: cross Abstract: Prompt injection is a leading security risk for LLMs and LLM-based applications such as agents. State-of-the-art red-teaming methods for prompt injection leverage reinforcement learning (RL) to train an attacker LLM to generate effective injected prompts. However, when targeting frontier LLMs such as GPT-6-Luna, a major challenge is the cold-start
arXiv:2609.33898v1 Announce Type: cross Abstract: Training efficiency has become the central driver of recent progress in foundation models. To overcome the massive computational and data requirements of large-scale training, researchers increasingly adopt strategies such as selective data sampling, efficient pre-training, and simplified reinforcement learning pipelines. While these strategies dra
arXiv:2609.33915v1 Announce Type: cross Abstract: Progress in machine learning cannot outpace our ability to verify it. With an explosion in papers today, every scientific claim rests initially on trust in the trainer, leading to uneven evaluation, baselines, and forestalling of reliable progress. Traditionally, the burden of verification falls on the reader, who must reproduce expensive training
arXiv:2609.34089v1 Announce Type: cross Abstract: Neural decompilers are machine learning models which perform the process of decompilation, lifting code from a lower-level language to a higher one. Neural decompilers offer substantial utility relative to traditional deterministic decompilers because they can probabilistically recover information discarded during lowering, like variable names, typ
arXiv:2609.34376v1 Announce Type: cross Abstract: Tool-using agents can initiate consequential infrastructure changes, yet evidence required for admission may expire while other checks run or depend on a shared fault domain. We formulate evidence acquisition as joint witness selection and scheduling under quorum, diversity, freshness, deadline, and resource constraints. Assurance-Aware Semantic Sc
arXiv:2609.35171v1 Announce Type: cross Abstract: Solana is one of the most popular blockchains, and is arguably the most widely used blockchain for smart contracts, also known as dApps. Understanding the types of smart contracts that are being executed by Solana and their interplay is therefore highly beneficial both for designers of modern blockchains and developers of smart contracts. To that e
arXiv:2609.35224v1 Announce Type: cross Abstract: Masked-diffusion language models fill in masked positions in parallel and in no fixed order. Most practical text watermarks assume left-to-right generation. They key each token to the tokens before it, and in a diffusion model those tokens may still be masked. A fixed green list needs no such context, but it favors the same tokens at every position
arXiv:2609.35339v1 Announce Type: cross Abstract: Zero-knowledge proofs (ZKPs) enable the verification of a statement without revealing any information beyond its validity and constitute a fundamental primitive in cryptography and information theory. However, existing constructions rely on computational assumptions and are predominantly confined to bipartite settings, leaving their information-the
arXiv:2504.20296v2 Announce Type: replace Abstract: Public blockchains record transaction histories that enable address clustering, taint analysis, and cross-service attribution, thereby motivating the development of mixers and privacy layers. Our work presents a structured scoping review of 22 representative systems, defining a common unlinkability objective and five adversary archetypes. We eval
arXiv:2505.07167v4 Announce Type: replace Abstract: Large Language Models (LLMs) have been extensively used across diverse domains, including virtual assistants, automated code generation, and scientific research. However, they remain vulnerable to jailbreak attacks, which manipulate the models into generating harmful responses despite safety alignment. Recent studies have shown that current safet
arXiv:2511.10863v2 Announce Type: replace Abstract: This paper presents a secure aggregation system Armadillo that has disruptive resistance against adversarial clients, such that any coalition of malicious clients (within the tolerated threshold) can affect the aggregation result only by misreporting their private inputs in a pre-defined legitimate range. Armadillo is designed for federated learn
arXiv:2511.15990v2 Announce Type: replace Abstract: Digital agriculture is transforming the way we grow food by utilizing technology to make farming more efficient, sustainable, and productive. This modern approach to agriculture generates a wealth of valuable data that could help address global food challenges, but farmers are hesitant to share it due to privacy concerns. This limits the extent t
arXiv:2605.06158v2 Announce Type: replace Abstract: Multi-step attacks on large language model agents may depend on opportunities distributed across sessions, such as access to target information or the availability of required tools. To combine these opportunities, an attack needs to retain its state and intermediate results, and choose actions based on current conditions. In this work, we study
arXiv:2605.16035v2 Announce Type: replace Abstract: AI agents increasingly act autonomously in the world, yet harmful behavior cannot be reliably traced to the account that deployed the agent. This creates an accountability gap across both benign and malicious settings: misconfigured or hijacked agents may cause unintended harm, while malicious operators may deploy agents for scams, harassment, or
arXiv:2607.00213v2 Announce Type: replace Abstract: This paper introduces the Federated Sovereign Transport Protocol (FSTP), a synchronization boundary and transport layer for federated networks in which nodes have heterogeneous privacy requirements. Existing federation protocols leave data confinement to operator policy: they define message formats and delivery semantics but impose no structural
arXiv:2607.11698v2 Announce Type: replace Abstract: Production LLM agents such as Claude Code and Codex can modify files and execute commands, so safety failures become real destructive actions. Automatic red-teaming lets safety teams test beyond static suites at the pace of deployment updates. Existing methods retain successful attacks but not why each attack succeeded, so after a failed reuse te
arXiv:2509.09215v3 Announce Type: replace-cross Abstract: Large language models (LLMs)-empowered autonomous agents are transforming both digital and physical environments by enabling adaptive, multi-agent collaboration. While these agents offer significant opportunities across domains such as finance, healthcare, and smart manufacturing, their unpredictable behaviors and heterogeneous capabilities
arXiv:2605.12794v2 Announce Type: replace-cross Abstract: The Ethereum blockchain utilizes the EIP-1559 algorithm to manage transaction inclusion and block assembly. However, EIP-1559 and much of the existing literature study this problem from a static perspective, focusing on price evolution without modelling transaction dynamics within the mempool. Motivated by this limitation, we study a dynami
arXiv:2605.13210v2 Announce Type: replace-cross Abstract: In this paper, we present PoisonCap: scalable temporal safety with strict use-after-free protection and initialisation safety for CHERI systems. Efficient memory safety is an increasing priority for programming languages, operating systems, and hardware designs, and CHERI is a leading hardware/software system that provides native spatial sa
arXiv:2605.19437v2 Announce Type: replace-cross Abstract: The Invisible Internet Project (I2P) is a peer-to-peer anonymous overlay network whose architecture includes a structurally distinct sublayer not characterized in existing security literature. We term this sublayer the Exclusive Network: nodes here host operational services and draw on I2P's routing resources, but publish no RouterInfo reco
arXiv:2607.16242v2 Announce Type: replace-cross Abstract: Fine-Tuning-as-a-Service (FTaaS) platforms let users perform supervised fine-tuning (SFT) on customized data, but this pipeline can erode model safety alignment. To recover safety without re-running full alignment, existing realignment methods focus on calibrating the integration of safety patches into fine-tuned models. These methods exhib
arXiv:2608.00882v2 Announce Type: replace-cross Abstract: Many high-level security requirements are about the allowed flow of information in programs and are difficult to make precise because they involve selective downgrading. Notions from epistemic logic have emerged as a good approach to policy semantics but a robust general framework remains elusive. A paper appearing in CSF 2018, entitled ``A
OpenAI's Models Accessed Public US Census, SEC Data ->Insurance Journal | More on "OpenAI's public data access practices" at BigEarthData.ai | #Data #OpenAI OpenAI’s artificial intelligence models accessed publicly available information from US government websites, including those of the Census Bureau and the Securities and Exchange Commission. The company’s agentic AI systems interacted with SEC.
Fico besta quando conheço alguém e, com o tempo, percebo que a pessoa inteira é um personagem (e nem é masking autista, ta?). É uma falsidade constante com ela mesma e com os outros. Ri de piada que ninguém achou graça, faz de tudo para engajar e, no segundo seguinte, por trás, joga os colegas aos leões (aqueles que a pessoa enxerga como obstáculo). Acredite, já tive que defender amigos desse tipo
One thing the security industry misses when throwing stones at OpenAI and Anthropic, is how much they're already making from Mythos and Codex for security. Orgs are paying *millions* every single month for the honor of using these models to find vulnerabilities in their code. They are taking the industry's lunch money, we just didn't wake up yet to *name* them as such. This reminds me of how we us