Search: “concurrency”

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Testing race conditions with memory access tracing and stack-based delay injection

Google Project Zero released MAccConc, Linux kernel tooling that traces memory accesses to explore and test race condition interleavings.

A Google Project Zero researcher published MAccConc (Memory Access Concurrency), tooling for exploring possible interleavings of multithreaded test cases in the Linux kernel, available on GitHub. The tools use KCOV with ASAN outline-mode instrumentation to record per-access memory traces, enabling automatic testing of all A-B-A interleavings plus terminal and GUI explorers for manual analysis. The work targets confirming race condition candidates, building reliable regression tests, and enabling concurrency fuzzing, drawing on ideas from SKI and Ned Williamson's sockfuzzer.

Google Project Zero·Sep 8, 07:00 UTC · 8d agoResearch1

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An Empirical Analysis of CodeQL False Positives and Query Refinements for Java Vulnerabilities

Study of 167 Java CVE instances finds CodeQL false positives follow recurring patterns; query refinements remove 81.8% of reviewed ones.

Researchers ran CodeQL's Java security query suite on 167 CVE instances from 110 projects, manually reviewing 500 sampled false-positive paths and building a five-category taxonomy led by Missed Path Constraint or Sanitization (36.6%), Benign Execution Context (29.4%), and Missing Trust Boundary Modeling (27.6%). Guided by the taxonomy, query-level refinements removed 81.8% of reviewed false positives and 15.8% of reported paths across the selected queries while retaining 7 of 8 true positives. To address generalization, agentic coding tools given the refinement patterns as templates adapted them to new projects successfully in 56% and 62% of tasks, versus 28% without guidance.

arXiv cs.CR·Sep 3, 22:48 UTC · 12d agoResearch1

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From Intent to Execution Grant: An Execution-Boundary Conformance Profile for High-Risk AI Actions

Researchers specify EBL-Core, an execution-boundary conformance profile binding AI agent intents, policies, and evidence into verifiable execution grants, validated with bounded tests.

The paper defines EBL-Core, a conformance profile deciding whether one fully materialized AI-generated candidate action may receive action-scoped execution authority. It binds a structured intent object, Root and Operational Policies, typed evidence, and a verifiable Decision Derivation through an Execution Release Contract, with lifecycle rules for Redemption and Revocation. Evaluation included 34 static vectors, 15 lifecycle checks, and 100 trials of 32 concurrent Redemption attempts yielding exactly one winner per trial. The authors state these bounded results demonstrate executability of the specified subset, not production readiness or complete mediation.

arXiv cs.CR·Sep 10, 14:21 UTC · 6d agoAI safety & security1

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TasmScan: Continuation-Aware Taint Analysis for TVM Bytecode with Savelist Abstraction

TasmScan introduces source-free taint analysis for TON smart-contract bytecode, detecting 95.3% of defects with 96.8% precision and 17x speedup.

TasmScan is the first bytecode-level static analysis framework for the TON Virtual Machine, enabling cross-continuation data flow reasoning without source code by modeling savelist semantics through forward register analysis with formal over-approximation guarantees. It lifts bytecode into a typed intermediate representation (TASIR) and performs path-sensitive taint analysis. On a 208-contract benchmark with human-confirmed ground truth it detects 95.3% of defects across five classes at 96.8% precision, and resolves 294,546 dynamic continuation targets with 100% precision across 2,921 registry contracts. It achieves a 17x median speedup over symbolic-execution baselines.

arXiv cs.CR·Sep 15, 11:01 UTC · 1d agoResearch

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Towards Standardized Evaluation of GPU Memory Safety with GMSBench

GMSBench provides 149 CUDA tests covering spatial, temporal, and concurrency GPU memory errors, exposing detection gaps in Compute Sanitizer.

GMSBench is a GPU memory safety benchmark comprising 149 self-contained CUDA tests spanning spatial, temporal, and concurrency errors across different GPU memory spaces and execution scenarios. The authors evaluate NVIDIA's Compute Sanitizer across multiple GPU architectures using the suite, exposing gaps in its detection coverage. The benchmark offers a standardized foundation for comparative evaluation of GPU memory safety mechanisms.

arXiv cs.CR·Sep 8, 15:11 UTC · 8d agoResearch

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CONTINUITY: Security-Context Contracts for Composable LLM Agent Controls

Researchers introduce CONTINUITY, a framework of assume-guarantee contracts that preserves LLM agent security context across components, verified across 2,560 attack instances.

The paper identifies security-context discontinuity, where individually sound controls drop, widen, or reinterpret security context as actions cross component boundaries, and proposes CONTINUITY, a framework of assume-guarantee contracts using signed root grants, provenance commitments, role-bound transition receipts, and effect-bound execution permits. It formalizes end-to-end consequence integrity, requiring every external effect to be backed by a valid authorization witness linking principal, task, provenance, and policy state. A reference verifier and cross-layer fault-injection suite covering 32 fault classes showed the full configuration committed no harmful external effect across 2,560 parameterized attack instances while completing all 700 benign tasks and escalating all 200 ambiguous cases.

arXiv cs.CR·Sep 4, 15:26 UTC · 12d agoAI safety & security

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Cohere Releases North Small Translate: A 218B MoE Translation Model That Scores 83.6 on WMT26 Across 50 Languages

Cohere released North Small Translate, an open-weight 218B MoE (25B active) translation model scoring 83.6 on WMT26 across 50 languages.

Cohere and Cohere Labs released North Small Translate, a decoder-only sparse Mixture-of-Experts translation model with 218B total and 25B active parameters, 128 experts with 8 activated per token plus shared experts, and 16K-token input and output context. In Cohere's vendor-reported WMT26 evaluation, judged by GPT-5.6-Sol, it scores 83.6 averaged across 50 languages (84.36 in an agentic multi-pass mode), ahead of DeepL NextGen (81.37), Qwen 3.5 397B A17B (81.56), GLM 5.2 (76.50), and Google Translate (68.20). The model was built with RWS's Language Weaver team, post-trained specifically for translation, and reports 112 output tokens per second versus 81 for Gemma 4 31B, with long-document xCOMET-XL scores of 48.9 versus 21.3 for Google Translate. It is available free on Cohere's Chat V2 API until rate limits, with three self-hosting checkpoints including a 4-bit NVFP4 variant running on 1x B200 or 2x H100.

MarkTechPost·Sep 11, 06:57 UTC · 5d agoModel release

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m-a-p/YuE2-3B — new model trending #30 on Hugging Face

M-A-P released YuE2-3B, an open music generation model that outperforms Suno v5 on WildSongBench and runs locally on a 24GB GPU.

The M-A-P (multimodal-art-projection) team released YuE2-3B, an open-weights music generation model that turns lyrics and a style prompt into full songs with vocals and accompaniment. It uses an AR-NAR Mixture-of-Transformers backbone with symbolic planning and flow matching through a VAE, and supports editable scores (melody and chords, including ABC notation) plus agentic editing workflows. On 192 WildSongBench prompts it reports a SongBench average of 6.9632 (best-of-8) versus 6.8721 for Suno v5, claimed as state of the art among evaluated open and proprietary models. It runs 48 kHz stereo inference locally on a single 24GB NVIDIA GPU without quantization, with companion releases including YuE2-Vae, MERT-v2 encoders, the WildSongBench dataset, and SheetSage2.

Hugging Face trending models·Sep 9, 18:30 UTC · 7d agoModel release1

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Characterizing Language Generation in the Limit: Finite Witnesses and a Separation-Width Hierarch

New work characterizes language generation in the limit via finite witnesses, proves a full separation-width hierarchy, and formalizes all results in Lean.

The paper fully characterizes when language generation in the limit is possible for arbitrary families over a countable universe: each target must admit a finite positive witness such that targets activated by any finite sample share an infinite common intersection. It defines positive separation width and proves every level of the resulting hierarchy occurs, with countable families admitting singleton witnesses and unions of families with infinite common cores requiring unbounded finite witnesses. The characterization, a universal normalization, and a diagonal capture lemma are machine-checked in the Lean proof assistant, with the development maintained on GitHub.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:54 UTC · 7d agoAI research1

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CDN Tsunami Attack Abuses HTTP/3 Translation for Up to 350x DoS Amplification

Researchers disclosed two DoS attacks exploiting CDNs' HTTP/3-to-HTTP/1.1 translation to amplify low-bandwidth requests up to 350x against origin servers.

Researchers named the two techniques HTTP/3 Bandwidth Amplification (HBA) and HTTP/3 Connection Amplification (HCA), exploiting the gap where CDNs speak HTTP/3 to browsers but only HTTP/1.1 to origins. All six tested CDNs (Alibaba, Baidu, Cloudflare, CloudFront, Fastly, Tencent) were susceptible to HBA, and five to HCA, with Cloudflare immune to HCA because it buffers full requests before opening backend connections. Attackers using under 5 Mbps forced over 100 Mbps at origins, and the 350x factor applies only to Alibaba, Baidu, and Tencent, which support the QPACK dynamic table. Testing identified 42,330 potentially vulnerable subdomains out of 151,685 hosted on the six providers across the Tranco Top 1M.

The Hacker News·Aug 20, 11:39 UTC · 27d agoResearch

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IntentFuzz: A Protocol-Aware Fuzzer for Automated Invariant Violation Detection in Intent-Based Cross-Chain Bridges

IntentFuzz protocol-aware fuzzer recovers bridge structure from unannotated Solidity and confirmed 22 invariant violations across 24 real-world deployments.

IntentFuzz formalizes a taxonomy separating invariant violations from settlement exposures in intent-based cross-chain bridges, then recovers a bridge's intent structure and deposit/fill function roles from unannotated Solidity source. It classified deposit and fill functions with 100% recall and 82% combined precision, and achieved 100% recall and precision on 23 planted-bug mutants. Across 24 real-world deployments it confirmed 17 genuine invariant violations with heuristic-only input generation, rising to 22 with its LLM-assisted tier, spanning eight vulnerable GitHub repositories with findings reproducible against public deployed bytecode.

arXiv cs.CR·Sep 11, 16:03 UTC · 5d agoResearch1

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Hillingar - MirageOS Unikernels on NixOS

A technical write-up details Hillingar, enabling reproducible Nix-based builds and NixOS deployment of MirageOS OCaml unikernels such as authoritative DNS servers.

This blog post (published December 2022, updated February 2025) describes Hillingar, work from the author's master's thesis enabling reproducible builds and deployments of MirageOS OCaml unikernels using Nix and a custom NixOS module, demonstrated with an authoritative DNS server. MirageOS unikernels embed application and low-level OS code in a single kernel, allowing dead-code elimination that reduces attack surface and improves efficiency. The post covers challenges such as solving opam dependency version constraints when linking a single dependency set.

Lobsters · security·Sep 4, 09:17 UTC · 12d agoTools1

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Forgetting Without Restarting: Execution-State Unlearning for Stateful LLM Agents

Researchers propose provenance-guided selective replay letting LLM agents forget revoked information without restarts, matching full reset behavior.

The paper formalizes execution-state unlearning for stateful LLM agents, requiring that agents behave as if a revoked memory record was never observed across transcripts, compressed memory, tool plans, and KV caches. It proves exact unlearning requires at least T-τ+1 recomputed transitions and that Provenance-Guided Selective Replay attains this bound via a provenance graph, KV cache cropping, and sanitized replay. In audits across three agent suites, nine baselines, and three model families, memory deletion left leakage unchanged, instruction-based forgetting collapsed under elicitation (Leak@probes = 1.00), and selective replay matched full resets at up to 9x fewer recomputed tokens.

arXiv cs.CR·Sep 4, 08:31 UTC · 12d agoAI safety & security1

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[AINews] OpenAI reports Navier-Stokes singularity find in 88 hours using Astra-next, roughly 10,000 agents and 130B tokens (>$40M), a contender for second ever Millennium Prize awarded

OpenAI-linked accounts claim roughly 10,000 AI agents produced a Navier-Stokes singularity result in 88 hours, pending mathematical verification.

OpenAI-affiliated accounts claim a system of roughly 10,000 agents, trained over about a year with multi-agent reinforcement learning, produced a finite-time singularity result related to the Navier-Stokes Millennium Problem. The claimed 88-hour runtime and 130B-token cost circulate only via social posts, and no preprint, theorem statement, or proof artifact is available. Acceptance by the mathematics community is unresolved, so the claim's epistemic status remains unknown. The roundup also notes Cognition's $48B and Mistral's $24B fundraises, GPT Image 2.5, and Meta's Muse agent relaunch.

Latent Space·Sep 9, 05:04 UTC · 7d agoAI research1

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Opaque recurrence, and other AI terms that you should probably know

TechCrunch updates its plain-English glossary defining common AI terms from AGI and agents to chain-of-thought reasoning.

TechCrunch maintains a regularly updated glossary of AI terminology, defining terms such as AGI, AI agents, API endpoints, chain of thought, coding agents, compute, deep learning, and diffusion. It highlights 'opaque recurrence', the reasoning technique in OpenAI's new Astra model that has drawn attention from AI safety researchers. The piece is an educational living document rather than new research or a product announcement.

TechCrunch · AI·Sep 7, 19:24 UTC · 9d agoAI industry1

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[AINews] 10% worse, 100x cheaper, 10000x faster: Why Simulation is taking over

Latent Space argues AI training pipeline stages—rewards, data, teachers, curricula, environments—are flipping from human-made to model-made simulation.

Latent Space's AINews essay traces how each component of AI training has turned synthetic since 2022: reward models (InstructGPT, RLAIF), synthetic pretraining data (Microsoft Phi, NVIDIA Nemotron-4 340B), model teachers (Alpaca, DeepSeek-R1 distillation), and self-generated curricula (Self-Rewarding Language Models, SPIN). In 2026 it highlights Karpathy's autoresearch loop—700 experiments yielding 20 kept improvements, cutting GPT-2 training time from 2.02 to 1.80 hours—and Z.ai's GLM-5.3 fully synthetic RL environment, judging, and verification stack. It frames these shifts as 'simulation': 10% worse but 100x cheaper and 10,000x faster than human equivalents.

Latent Space·Aug 22, 07:36 UTC · 25d agoAI industry

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Heterogeneous Cross-Chain Transaction Tracing for Solana Bridges via Candidate-Set Selective Decision

SolTracer traces cross-chain transactions onto Solana bridges, improving open-world association F1 by 20.16% over the strongest baseline for illicit-fund tracing.

The paper formalizes four Solana-bound cross-chain transaction modes and proposes SolTracer, which maps heterogeneous execution semantics into a unified event space and uses candidate-set selective decision-making with abstention when valid targets are absent. In the challenging open-world setting with a 50% TA ratio, SolTracer improves F1 by 20.16% over the strongest baseline. An empirical study of real-world transfers examines count-value divergence across bridge mechanisms, cross-asset shifts, and decoupling between on-chain settlement and explorer visibility.

arXiv cs.CR·Sep 10, 11:43 UTC · 6d agoResearch

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Researchers open-source a Wi-Fi cyber range for security training

NTNU and Aegean researchers open-source a software-emulated Wi-Fi cyber range using mac80211_hwsim with LLM-assisted scenario building.

Researchers from the Norwegian University of Science and Technology and the University of the Aegean published a design and prototype for a cyber range dedicated to IEEE 802.11 security training, emulating access points and clients with mac80211_hwsim, Linux namespaces, hostapd, wpa_supplicant, dnsmasq, and FreeRADIUS. The platform bundles Aircrack-ng, Wireshark, and custom tools WPAxFuzz and Bl0ck, and can convert plain-language scenario descriptions into deployable definitions via a locally hosted Llama model. A working prototype covering scenario creation and deployment is on GitHub; monitoring, access control, and orchestration zones remain future work.

Help Net Security·Aug 24, 04:21 UTC · 23d agoTools1

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CHERI-D Reincarnate: efficient multicore CHERI temporal memory safety through allocation reincarnation (draft version)

CHERI-D Reincarnate adds allocation reincarnation to CHERI, quarantining generation IDs instead of memory to cut temporal memory-safety overheads on multicore systems.

The proposal extends CHERI-D temporal memory safety by quarantining exhausted generation IDs rather than memory slots, allowing freed memory immediate reuse through allocation reincarnation. It adds coherent ID caching and multicore support connecting physical coherence events to the virtually addressed ObjID buffer. The design was implemented as a hardware-software co-design across CHERI-Toooba FPGA softcore, QEMU, LLVM/Clang, and CheriBSD. Across evaluated workloads it substantially reduces memory-sweep frequency and quarantine overhead with low performance and hardware cost.

arXiv cs.CR·Sep 10, 14:19 UTC · 6d agoResearch

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HoneyRoute: Honeypot-Model Routing for Adversarial LLM Serving

HoneyRoute detects malicious LLM serving requests and diverts them to a honeypot model, reaching F1 0.911 with 38 ms median added latency.

HoneyRoute is an inference-serving layer pairing a streaming router (a frozen 0.8B embedding backbone with per-domain MLP heads) with a dual-implementation honeypot and an analysis loop that converts trapped interactions into attacker fingerprints for router retraining. On a production trace plus a seven-domain attack corpus it matches 96% of a two-tier guard-LLM cascade's F1 at 1/385th of its latency with 0% evasion under 13 adversarial transformations. Diverting malicious traffic cuts production token consumption under GCG-suffix flooding by 97.8%, and loop training raises detection F1 to 0.933.

arXiv cs.CR·Sep 8, 06:26 UTC · 8d agoAI safety & security

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Closing the Loop: Bidirectional Fully Encrypted Protocols

Researchers show naively composing unidirectional fully encrypted protocols is detectable and construct provably secure bidirectional FEPs, validated in Rust.

The paper introduces formal security definitions for bidirectional fully encrypted protocols (BiFEPs), covering exact shaping, delivery, protocol-state integrity, private half-close, and cross-direction isolation. It shows trivially composing two unidirectional FEPs enables detection attacks via cross-direction dependencies like traffic imbalance and connection tear-down. The authors construct provably secure BiFEPs for datastream and datagram settings, validated with a Rust implementation; no surveyed deployed protocol provides the full set of properties.

arXiv cs.CR·Sep 15, 16:32 UTC · 1d agoResearch

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GPT-6 Astra: an automated AI Engineer you can hire for <$6 an hour

OpenAI launches GPT-6 Astra, a frontier model scoring 97.6% on FrontierMath and 99.9% on ARC-AGI-3, capable of autonomous AI engineering at roughly $6 per hour.

OpenAI launched GPT-6 Astra, described as its first Stargate and lightly looped frontier model, beating Fable 5.1 on many metrics and saturating the hardest FrontierMath (97.6%) and ARC-AGI-3 (99.9%) benchmarks. Latent Space tested the model with over 20 billion tokens, reporting it can train and select models, label data, deploy and debug systems, and orchestrate 20-50 parallel subagents. The authors measured about $6 per hour of agentic engineering at 33 tokens per second, with token efficiency independently confirmed by Artificial Analysis.

Latent Space·Sep 3, 21:09 UTC · 13d agoModel release1

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TRACE: Trajectory-robust Admission with Evidence Ordering for Efficient GUI Agents

TRACE, a training-free visual token pruning framework, cuts GUI agent inference latency and memory while keeping trajectory-wide visual evidence reusable.

TRACE is a training-free framework for trajectory-robust admission and coverage-aware evidence ordering that prunes high-resolution screenshot tokens accumulated in GUI agent trajectories. It ranks visual evidence using a query-independent layout-derived interaction prior combined with instruction relevance and feature novelty, and reserves part of the budget for native tokens distributed across the screen to repair spatial coverage. A monotone KV contraction incrementally compresses retired frames into compact session state, avoiding repeated visual encoding or pruning. Experiments across six GUI benchmarks and diverse models verify effectiveness under tight budgets, with source code to be released.

Hugging Face daily papers·Sep 8, 20:00 UTC · 8d agoAI research

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Beyond the Turing threshold: Productive grammars generate essentially undecidable languages

A theoretical paper designs formal grammars that emulate Post's productive sets, generating languages that are provably beyond Turing decidability.

The paper elaborates on Emil Post's productive sets, which are not even semi-computable, and builds formal grammars that emulate their construction over natural numbers. The resulting languages are shown to be essentially undecidable, placing them beyond Turing decidability. This is pure computability and formal language theory with limited direct security relevance.

arXiv cs.CR·Sep 10, 11:20 UTC · 6d agoResearch1

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BlueSTAR: Tiered Agentic Architecture for Autonomous Cyber Defense

BlueSTAR is a tiered agentic LLM architecture for autonomous cyber defense, validated on live enterprise IT/OT cyber ranges against seven attack chains.

Researchers present BlueSTAR, a tiered agentic architecture for autonomous cyber defense in enterprise IT/OT networks that transforms high-volume security telemetry into compact indicators of compromise. It pairs deterministic containment for known threats with LLM reasoning for attacks requiring contextual and cross-cycle analysis, and introduces a resilience metric jointly weighing attacker reach, mission-critical impact, and defensive disruption. Evaluation on two live cyber ranges with seven attack chains based on real-world intrusion techniques covered credential theft, repeated compromise, concurrent attackers, and attacks on physical processes.

arXiv cs.CR·Sep 10, 17:36 UTC · 6d agoResearch

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MasterControl Seventeen Every Time

Governed enterprise analytics study shows deterministic policy execution matched 110/110 answer-and-evidence contracts while runtime agent planning matched none.

The paper studies a governed approach where a language model interprets the question while deterministic policy selects and runs a pre-approved analytical program returning results and evidence. Across 440 runs, three 8B models generated SQL and selected tools at runtime, while Qwen3-8B only interpreted intent and policy executed the approved program. None of 330 runtime-planning episodes satisfied the full answer-and-evidence contract, whereas the policy-executed analyzer matched 110 of 110. The authors note this is configuration-specific and expressiveness is preserved via relational operations, aggregation, comparison, windows, ranking, and similarity with replayable results.

Hugging Face daily papers·Sep 1, 20:00 UTC · 15d agoAI research

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TuxBot v3: Inside an IoT Botnet Framework With LLM

Unit 42 uncovers TuxBot v3, an LLM-assisted IoT botnet framework with 17-architecture builds, Telnet brute-forcing, and DDoS capabilities.

Palo Alto Unit 42 identified TuxBot v3 Evolution, a modular IoT botnet framework derived from AISURU, Wuhan-lineage botnets, and MHDDoS. The C-based bot brute-forces Telnet with 1,496 credential pairs, targets over 30 IoT device families, and communicates with a Go-based C2 over encrypted TCP with multiple fallback mechanisms including DGA, P2P, and DNS TXT. LLM-assisted development left hallucinated crypto implementations and broken exploit modules in the analyzed samples, though roughly 70% of core functionality works. Researchers warn polished production builds likely exist, raising the threat potential.

Palo Alto Unit 42·Aug 19, 12:03 UTC · 28d agoMalware1

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Procedural Graphs: Self-Evolving Execution Structures for LLM Agents

Researchers introduce Procedural Graphs, self-evolving (procedure, relation, procedure) structures guiding LLM agent tool use and planning.

Procedural Graphs organize procedural knowledge into (procedure, relation, procedure) triplets to guide LLM agent actions, addressing drift such as lost objectives, out-of-order tool calls, and repeated unproductive steps. At each decision step the framework localizes the active node and a guidance model translates the surrounding subgraph into step-level situational guidance. An LLM refiner edits graph topology by contrasting failed with successful trajectories, and across datasets, task types and LLMs the approach outperforms memory-based baselines and matches or surpasses hand-designed graphs.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:59 UTC · 8d agoAI research1

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Angry Birds: Toy Ghouls’ new toys

Kaspersky tracks ransomware group Toy Ghouls deploying new custom backdoors, mqtt-bird-agent and matrix-bird-agent, using HiveMQ MQTT and Element as C2 channels.

Kaspersky reports that Toy Ghouls (also known as Bearlyfy, Laboo.boo and Feral Wolf), a financially motivated group targeting Russian organizations since 2025, shifted from leaked Babuk and LockBit builders and public GitHub tools to its custom GenieLocker ransomware. In early July 2026 the group deployed two custom backdoors: mqtt-bird-agent 0.1.0 using the public HiveMQ MQTT broker as C2, and matrix-bird-agent 0.1.0 using the Element messenger. The backdoors are delivered via WinRM using Evil-WinRM and WinRM-fs, persist as Windows services, and encrypt their configuration with ChaCha20-Poly1305 keyed to the machine's MachineGuid registry value.

Kaspersky Securelist·Sep 4, 10:00 UTC · 12d agoThreat actor

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LLMs and Contextual Integrity

Bruce Schneier highlights two papers: the CIMemories benchmark shows frontier LLMs leak memory attributes up to 69%, and an RL method reduces inappropriate disclosures.

Bruce Schneier discusses contextual integrity in LLMs, referencing the CIMemories benchmark, which uses synthetic profiles with 100+ attributes per user to test whether models with persistent memory disclose sensitive information appropriately. Evaluation showed frontier models exhibit up to 69% attribute-level violations, with GPT-5's violation rate rising from 0.1% to 9.6% across 40 tasks and reaching 25.1% with repeated prompting, showing unstable leakage behavior. A second paper introduces a reinforcement learning framework trained on a synthetic 700-example dataset that substantially reduces inappropriate disclosure while maintaining task performance, with improvements transferring to the human-annotated PrivacyLens benchmark.

Schneier on Security·Aug 18, 10:40 UTC · 29d agoAI safety & security

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The extension you never installed: KREMLIN forges Chrome's own integrity checks to steal banking sessions

Elastic details KREMLIN, a Brazilian banking malware whose malicious Chrome/Edge extension forges Chromium integrity checks to steal sessions.

Elastic Security Labs tracked operation REF9334 and its KREMLIN toolkit, a Brazilian banking malware campaign impersonating twelve banks across seven campaigns since May 2025. The infection chain uses multi-stage obfuscated JavaScript loaders with sandbox checks (file and process counting), scheduled-task persistence, and an Ethereum smart contract acting as a dead-drop resolver for C2 and payload URLs, abusing Archive.org for hosting. The malicious browser extension bypasses Chromium integrity mechanisms by manipulating Secure Preferences and regenerating required HMACs and App-Bound encrypted hashes. Threat Command disrupted over 1,500 infections by registering the kill-switch canary domain.

Elastic Security Labs·Sep 14, 00:00 UTC · 2d agoMalware in the wild

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Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs

Fortunate Recall introduces ontology-based lifecycle policies for LLM memory, cutting confabulation roughly in half (e.g., 45.1% to 22.4%) versus Mem0.

Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and applies category-specific lifecycle rules including differential temporal decay, slot-key supersession, event-time validity, and retrieval routing. FR-Bank scores 76.9% on the new 516-question LifecycleBench, ahead of Mem0, A-MEM, Memory-R1, and MemoryOS (61%-70.5%), and 75.2% on LongMemEval-S. End-to-end, confabulation drops from Mem0's 45.1% to 22.4% over answered queries, with the ranking replicating on open-weight Kimi K2.5 and transferring to the independent BEAM benchmark (46.8% vs 32.9%).

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:31 UTC · 7d agoAI research

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OpenAI Agents API

OpenAI's Agents API documentation describes a managed Codex harness offering sandboxed agents, MCP connectivity, subagents, and US-only data residency.

The Agents API lets applications run durable agent sessions while OpenAI manages orchestration, context compaction, and recovery on the Codex harness. Agents can execute code, edit files, and connect to MCP servers in OpenAI-hosted or self-hosted sandboxes. Example applications include an incident response agent, Slack bot, data analyst, and GitHub issue investigator. Billing follows model, tool, and container rates; the examples use model gpt-6-astra.

Hacker News · security·updated Sep 11, 08:11 UTC · 5d ago·first Sep 10, 19:43 UTC · 6d agoAI tools & infra 4 sourcesHN 20↑ · 16 comments1

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C*: Unifying Programming and Verification in C

Research paper C* (arXiv:2504.02246) proposes unifying programming and formal verification in C, discussed on Hacker News.

The arXiv paper 2504.02246, titled C*: Unifying Programming and Verification in C, presents an approach that combines C programming with built-in formal verification. The item surfaced via Hacker News, where it reached 28 points and 18 comments. Detailed abstract content was not included in the source text.

Hacker News · security·Sep 8, 15:56 UTC · 8d agoResearchHN 28↑ · 18 comments

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What 90 days and a small budget can buy in AI agent security

Versa Field CISO details hidden costs of self-hosting open-weight models and a 90-day AI agent security plan of inventory, blast-radius reduction and testing.

In a Help Net Security interview, Prasad Tharippala, Field CISO at Versa, argues running open-weight models in-house improves control but shifts hardening, patching, access control, monitoring and incident response onto the buyer, with underestimated costs in GPU infrastructure, licensing review, EU AI Act compliance and scarce AI/ML security skills. On red-teaming AI agents, he recommends testing prompt injection, indirect injection, excessive permissions, data leakage, memory and RAG poisoning, malicious tool outputs, cross-agent trust abuse and infrastructure attack paths, mapped to OWASP agentic guidance and MITRE ATLAS. He highlights the handoff between chained agents as a major risk zone and stresses exercising human approval, shutdown and rollback controls under test conditions. For teams with 90 days and small budgets, he ranks inventory, blast radius reduction and ongoing testing as the priority order.

Help Net Security·Aug 28, 00:00 UTC · 19d agoAI safety & security

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SEMA-GUARD: Semantic and Graph-Based Vulnerability Detection in Assembly Code

SEMA-GUARD uses semantic analysis and graph neural networks to detect vulnerabilities in assembly code, achieving 85.1% accuracy on a Juliet-derived benchmark.

SEMA-GUARD is a framework that detects vulnerabilities in compiled programs when source code is unavailable, targeting malware, firmware, and embedded systems analysis. It enriches control flow graphs with low-level execution semantics including stack manipulations, memory accesses, and data flow. Evaluated on a Juliet Test Suite set compiled to assembly and split into function-level chunks, it achieves 85.1% accuracy and an F1 score of 0.801, outperforming purely statistical or structural approaches.

arXiv cs.CR·Sep 15, 14:35 UTC · 1d agoResearch1

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Client-Side Probing of Deleted Ridge Statistics in Federated Unlearning

Researchers show malicious federated learning clients can probe broadcast classifiers to recover deleted samples, exposing exact label leakage on MNIST and CIFAR-10.

The paper shows that federated unlearning systems broadcasting updated linear classifiers leak compact additive training summaries to clients. A malicious client can submit known changes, identify server states from returned classifiers, and compare states around an isolated deletion to expose the deleted sample, class, or client summary, potentially enabling reinsertion. On MNIST and CIFAR-10, high-precision broadcasts allowed exact label recovery for every tested deletion, while lower precision sharply reduced fine-grained recovery.

arXiv cs.CR·Sep 3, 20:59 UTC · 13d agoResearch

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On Identifying Sound Conditions for Frontrunning Resistance

Researchers formally define smart-contract frontrunning resistance, showing 55% of 393 audited vulnerabilities escape state-of-the-art detection, and find two undisclosed Ethereum flaws.

The paper gives the first formal definition of frontrunning vulnerability for smart contracts, grounded in how honest users interact with contracts rather than contract code alone. In a large-scale study of 287 smart contract audits, 55% of the 393 vulnerabilities reported by leading auditors fall outside the scope of state-of-the-art dynamic detection criteria. The authors present a sound algorithm for synthesizing secure interaction conditions and apply it to real-world contracts, uncovering previously undiscovered vulnerabilities in two Ethereum contracts.

arXiv cs.CR·Sep 10, 13:35 UTC · 6d agoResearch

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Revoked but Still Authoritative: An Empirical Study of Revocation Enforcement in Agent-Memory Systems

An empirical study finds no major agent-memory system enforces fact revocation at retrieval, causing agents to act on superseded, unsafe information.

Researchers tested five agent-memory systems across nine policy scenarios, nine models, and six defense conditions, tracking whether revoked facts are returned and acted upon. No system enforces revocation by default: revoked records are returned whenever the revocation label is visible to the retrieval layer, outrank their replacements, and lead agents to unsafe actions. The authors propose a backend-agnostic guard that sits between the agent and any memory store and withholds revoked or conflicting records at retrieval time.

arXiv cs.CR·Sep 8, 05:03 UTC · 8d agoAI safety & security

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Tell HN: OpenAI brings back 5 hour limit for plus and business standard users

OpenAI reinstated a 5-hour usage limit for Plus and Business Standard subscribers, sparking Hacker News debate about subsidized inference pricing and subscription value.

A Hacker News discussion reports that OpenAI has brought back a 5-hour usage limit for Plus and Business Standard users. Commenters debated whether cheap tokens are a subsidized customer acquisition strategy, whether AI companies have sustainable margins on inference, and how switching costs between providers affect dependency. The thread reflects community sentiment on pricing and usage caps rather than an official policy announcement.

Hacker News · security·Sep 7, 16:40 UTC · 9d agoAI industryHN 126↑ · 142 comments

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