Search: “persistent memory”

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SeverityAll critical 6 high 55 medium 31 low 2 info 49

MemSentry: A Framework for Detecting Persistent Memory Poisoning in Agentic AI

MemSentry intercepts persistent-memory writes in agentic AI to catch memory poisoning, reaching 91.7% accuracy with SBERT+LR classification.

Memory poisoning lets adversaries plant crafted content in an agent's long-term memory to suppress security alerts, enable privilege escalation, or override policies without modifying model weights or system prompts. The paper presents MemSentry, a configuration-driven framework that evaluates proposed persistent-memory writes on source trust, semantic risk, attack radius over a dependency DAG, access risk, and a signed security-state delta to issue deterministic Accept, Review, or Quarantine decisions. Across 1,000 GPT-4-generated scenarios on a 20-asset dependency DAG, SBERT+LR achieved 91.7% accuracy and 0.908 macro-F1, all four classifiers detected 100% of external quarantine-class threats, and verified-insider writes are escalated for human review rather than auto-quarantined.

arXiv cs.CRSep 8, 13:40 UTC · 8d agoAI safety & security

Microsoft Copilot Personal Flaws Could Let One Click Exfiltrate Data From Connected Apps

Varonis discloses CoSnitch (CVE-2026-24301), three Microsoft Copilot Personal flaws enabling one-click exfiltration of connected-app data; patched August 18, 2026.

Varonis Threat Labs found that an undocumented autorun=1 parameter, paired with the q parameter, lets an attacker-supplied prompt run automatically on page load in a victim's authenticated Copilot session, then exfiltrate data from connected services such as mail, calendar, Google Drive, chat history and the memory store via Copilot's built-in URL fetch to an attacker webhook. A separate memory-poisoning path through web summarization lets a crafted page persist attacker instructions in the user's memory, surviving password changes, session revocation and device re-enrollment. Microsoft shipped patches on August 18, 2026, tracked as CVE-2026-24301, and Varonis found no evidence of in-the-wild exploitation. The flaws were found via 'meta-hacking', asking Copilot itself to reveal the autorun parameter and its protections.

The Hacker NewsAug 18, 17:51 UTC · 29d agoAI safety & security CVE-2026-24301 CVE-2026-242991

Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems

16-day multi-agent stress test finds no world fully resilient to prompt injection, misinformation, or memory exposure; adversarial content acted on 46 hours later.

Emergence World is a continuously running multi-agent environment for adversarial stress testing of long-horizon autonomous systems. Eight parallel 10-agent worlds (seven homogeneous frontier-model worlds plus one mixed-model world) ran for 16 days, generating over 850,000 LLM calls and nearly 50 billion tokens. Three controlled stress events—indirect prompt injection, misinformation, and exposure of private agent memories—were delivered through ordinary interaction surfaces; no world achieved full resilience. Detection did not ensure containment: agents recognized threats yet wrote adversarial content into persistent memory and acted on it up to 46 hours later, suggesting model-level alignment is not compositional.

Hugging Face daily papersSep 14, 20:00 UTC · 1d agoAI safety & security

Show HN: Engrim – A universal, local-first SQLite memory engine for AI CLIs

Developer launches Engrim, an open-source local-first SQLite memory engine giving AI CLI agents persistent memory.

Engrim, shared on Hacker News (89 points), is a universal, local-first memory layer built on SQLite for AI CLI tools. It targets agent-style CLI applications that need durable cross-session memory without cloud dependencies. Details beyond the repository description were not provided in the source text.

Hacker News · AISep 7, 04:49 UTC · 9d agoAI tools & infra HN 89↑ · 53 comments2

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 SecurityAug 18, 10:40 UTC · 29d agoAI safety & security

The VMs Powering Mobile Agents (Instinct, Claude Code)

A teardown reveals Claude Code runs in Firecracker microVMs with a Rust PID 1 and MITM'd egress, while Instinct rents E2B sandboxes with git-based memory.

The author inspects the virtual machines hosting cloud agents: Claude Code runs in a Firecracker microVM with a custom Rust init (process_api) as PID 1, a 324 MB Bun harness on a read-only disk, and 443-only MITM'd SSE egress to api.anthropic.com with host-rotated OAuth tokens and no inbound access. Instinct rents E2B sandbox-as-a-service Firecracker microVMs (Ubuntu 22.04, 2 vCPU, 1.9 GB RAM) where agent memory is a git repo of Markdown committed by the agent and pushed to S3 as a single bundle, using short-lived STS credentials. Both platforms rely on Firecracker, differing mainly in fleet operator and guest boot configuration.

Hacker News · AISep 8, 04:36 UTC · 8d agoAI tools & infra HN 58↑ · 19 comments1

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.CLSep 9, 16:31 UTC · 7d agoAI research

Context Engineering Inside the Harness: 4 Mechanisms That Beat Context Overflow and Goal Loss on Long-Horizon Tasks

Survey of four harness mechanisms—context budgeting, compaction, todo-state, and memory—that keep long-horizon LLM agents on task across 200+ tool calls.

The article details how agent harnesses, not larger context windows, solve context overflow and goal loss on long-horizon tasks, citing Chroma's Context Rot report showing 18 LLMs (GPT-4.1, Claude 4, Gemini 2.5, Qwen3) degrade on long inputs. Concrete implementations include LangChain Deep Agents offloading tool responses over 20,000 tokens to the filesystem and truncating old tool calls at 85% window usage, and Claude Code capping auto memory at 25KB while re-reading the 5 most recently modified files after compaction. OpenAI's Responses API now offers server-side compaction via context_management with a standalone /responses/compact endpoint, which Codex uses for long-running coding tasks. Manus reports a roughly 100:1 input-to-output token ratio per ~50-tool-call task, motivating todo.md state recitation to prevent goal drift.

MarkTechPostSep 13, 05:56 UTC · 3d agoAI research1

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.CRSep 8, 05:03 UTC · 8d agoAI safety & security

ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding

ReactVAU is a slow-fast streaming framework for real-time video anomaly understanding that reserves heavyweight MLLM reasoning for suspicious events, improving efficiency.

ReactVAU addresses causal streaming video anomaly understanding with three components: a lightweight Fast Detection Module using Spatial Grid Folding, Anomaly-Aware Persistent Memory that protects critical visual cues from temporal decay, and a Slow Reasoning Module activated only on suspicious events. This design minimizes heavyweight MLLM invocations during long normal intervals. Experiments show competitive anomaly detection and causal reasoning under strict streaming constraints with significantly enhanced computational efficiency.

Hugging Face daily papersSep 6, 20:00 UTC · 9d agoAI research

MeClear: Cooperative Game-Theoretic Attribution and Risk-Aware Memory Clearance for Long-Horizon LLM Agents

MeClear uses cooperative Shapley attribution to clear harmful memories from long-horizon LLM agents, boosting task recovery by 25.5 points over baselines.

The paper introduces MeClear, a task-conditioned memory clearance framework for long-horizon LLM agents that identifies and selectively suppresses memories with negative downstream utility without permanently altering the persistent memory bank. It combines Leave-One-Out screening with sampled cooperative Shapley attribution to distribute utility across interacting evidence, resolving redundant conflict masking that single-removal evaluations miss. Across ten long dialogue memory pools it achieves 85.9% target recall and 82.3% overall task recovery, a 25.5 percentage-point improvement over LOO baselines.

arXiv cs.AI / cs.LG / cs.CLSep 8, 17:46 UTC · 8d agoAI research

AWS Introduces Pizza Bot: An Open Source Inbox for Background AI Agents

AWS open-sourced Pizza Bot, a self-hosted inbox app for background AI agents with approval gating and multi-provider model support.

AWS released Pizza Bot under Apache 2.0 after earlier versions served over 2,000 Amazon employees for meeting prep, email drafting, and research. The app provides macOS, Windows, and Linux desktop builds plus browser and terminal clients talking to a Hono API server, with LangGraph/DeepAgents checkpoints preserving thread state and approval pauses. It supports Amazon Bedrock, Anthropic, Google Gemini, OpenAI, OpenRouter, and Ollama, exposes external tools via MCP servers, and lets skill authors gate actions behind approve/edit/reject flows.

MarkTechPostSep 13, 08:10 UTC · 3d agoAI tools & infra

Toward Secure AI-Powered Penetration Testing Agents: Security Threats, Guardrails, and Architectural Perspectives

Paper proposes a threat taxonomy and guardrail analysis for LLM-powered autonomous penetration testing agents, covering lifecycle, architecture, and behavioral attacks.

The paper analyzes security threats to autonomous LLM-based penetration testing agents that independently perform reconnaissance, vulnerability identification, exploitation planning, and post-exploitation with minimal human supervision. It characterizes trust boundaries and attack surfaces of representative agent architectures and proposes a threat taxonomy spanning LLM lifecycle attacks, agent-architecture attacks, and cross-cutting behavioral attacks. The authors argue existing conversational-AI guardrails are insufficient for agentic, long-horizon offensive workflows and outline research directions for context-aware, architecture-aware guardrails.

arXiv cs.CRSep 15, 06:19 UTC · 1d agoAI safety & security

10 most critical LLM vulnerabilities

OWASP updated its Top 10 LLM application vulnerabilities, ranking prompt injection first and elevating excessive agency to third amid agentic adoption.

OWASP refreshed its Top 10 list of critical vulnerabilities in LLM applications, for the first time incorporating real-world incident data alongside expert voting. Prompt injection and sensitive information disclosure remain first and second, while excessive agency jumped from sixth to third as agentic systems that call APIs and execute code proliferate. Unbounded consumption of AI resources rose in prominence, while improper output handling dropped to the bottom as output sanitization becomes widespread. The list includes remediation guidance such as strict output schemas, human-in-the-loop approvals, and least-privilege credentials held in application code.

CSO OnlineSep 10, 08:25 UTC · 6d agoAI safety & security

Give Your Coding Agents a Memory You Own

Hugging Face introduces Funes, a tool that gives coding agents persistent, self-owned memory outside vendor clouds.

A Hugging Face blog post presents Funes, an approach for giving coding agents a persistent memory that developers own and control. The piece targets agent workflows where context must survive across sessions without ceding data to third-party services. No article body was available in the feed, so specifics beyond the title are limited.

Hugging Face BlogSep 3, 00:00 UTC · 13d agoAI tools & infra1

The Evolution of the Agent Harness

Latent Space essay argues late-2025 agent gains came from models and harnesses maturing together, with harness logic absorbed into model weights.

The piece defines the agent harness as everything beyond model weights—tools, context, memory, guardrails—and charts its evolution from ReAct prompting (October 2022) through AutoGPT's premature autonomy, Cursor/Copilot's human-in-the-loop retreat, and Devin's roughly 15% success rate, to o1's capability overhang and Claude Code's February 2025 terminal agent with permission rules. It argues the Christmas 2025 jump cited by Transformer co-inventor Lukasz Kaiser reflected model and harness curves crossing, and that remaining harnesses will serve human attention rather than the model.

Latent SpaceAug 22, 07:30 UTC · 25d agoAI tools & infra

When the prompt becomes the payload: A practical pen-testing guide for GenAI, LLM and RAG applications

CSO Online publishes a practical penetration-testing guide for GenAI, LLM, and RAG applications, covering prompt injection, retrieval poisoning, and tenant isolation testing.

The guide frames LLM applications as attack graphs spanning prompts, retrieval layers, vector stores, tools, identities, and downstream APIs, arguing that conventional web testing misses instruction-vs-data channel risks. It builds on OWASP prompt injection guidance (direct vs. indirect injection) and NIST's 2025 adversarial machine-learning taxonomy, noting that RAG and fine-tuning do not remove injection risk. Recommended practices include documenting trust transitions across components, using canaries and synthetic records to avoid test side effects, running multi-turn and obfuscated injection campaigns, and verifying chains from poisoned documents to observable state changes. It also details testing RAG pipelines via controlled document poisoning across metadata, OCR layers, and code comments, plus cross-tenant isolation checks on retrieved document IDs.

CSO OnlineSep 9, 10:00 UTC · 7d agoAI safety & security1

Another Blueprint In The Wall: How to Ask Frontier AI Like a Kid?

Six frontier models from OpenAI, Anthropic, xAI, and Google DeepMind converge on one imagined successor architecture when asked under a school-audience framing.

Researchers ran ten independent sessions per model type across six frontier models using a three-stage prompt sequence progressing to a full ASCII backbone architecture. Under school-audience framing, responses repeatedly converged on a shared motif including persistent latent state, adaptive computation, memory, specialist routing, verification, and stopping control, while control runs without the framing produced heterogeneous responses. A GPT-5.6 Sol output closely overlapped an architecture independently sketched by GPT-6 Astra, raising questions about shared design priors or motif propagation between model families. The paper coins 'epistemic jailbreak' for the observed loss of provenance discipline as prompt specificity increases.

Hugging Face daily papersSep 12, 20:00 UTC · 3d agoAI research 10 sources

[AINews] Poolside gets $12B reverse-execuhire to NVIDIA; founders stay for $1B, employees go for $6B, Infraco scaling to 7GW neocloud

NVIDIA struck a $12B deal with AI coding startup Poolside, licensing its Model Factory and hiring 109 of its technical employees.

NVIDIA spent roughly $12B in an unusual reverse-execuhire of Poolside, licensing the company's Model Factory while hiring 109 of its ~115 technical staff; founders retain a $1B stake and employees receive about $6B. Poolside had raced to raise $2B to fund a 40,000 GB300 cluster after missing a six-week funding window, and founders argue frontier-scale training now requires an order of magnitude more compute plus contracted data center space. An infrastructure arm spun out in January 2026 is scaling toward 7GW as a neocloud. The newsletter also recaps OpenAI and Anthropic agent-platform releases.

Latent SpaceAug 21, 05:45 UTC · 26d agoAI industry

HarnessVLN: Unifying Training-Free Embodied Navigation through an Agent Harness

Researchers introduce HarnessVLN, a zero-shot training-free agent harness that sets new training-free SOTA on vision-language navigation benchmarks including R2R and HM3D.

HarnessVLN is a zero-shot, training-free framework for embodied vision-language navigation that coordinates perception, retrieval, grounding, navigation, recovery, and termination through a unified tool interface. It validates planner proposals against spatial evidence, geometric feasibility, and subgoal consistency, using hierarchical event memory and a persistent Spatiotemporal Graph that stores reusable spatial evidence and failure annotations. It reports success rates of 60.8% on R2R, 53.9% on RxR, 76.0% on HM3D-v2, and 59.3% on HM3D-OVON, surpassing prior training-free state of the art, with real-world humanoid deployment demonstrated.

Hugging Face daily papersSep 13, 20:00 UTC · 2d agoAI research

When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems

Researchers propose a formal framework for governed proactive AI agency, linking activation decisions to continuing authorization, accountability, and bounded delegation in symbiotic systems.

The paper defines the activation problem: persistent AI assistants must decide whether a situation warrants behavior, and whether to act, ask, monitor, defer, or deliberately refrain. It distinguishes autonomous from delegated agency and defines symbiotic agency as delegation under a standing, revocable mandate, coupled to the principal's situation with calibrated inference and bounded personalization. The framework links activation decisions to authorized perception, behavior selection, authority containment, traceable restraint, and constrained adaptation, and provides an agency classification method, evaluation framework, benchmark scenarios, and reference architecture for always-present assistants and embodied support systems.

arXiv cs.AI / cs.LG / cs.CLSep 7, 16:46 UTC · 9d agoAI safety & security

How Fyxer built an AI executive assistant people trust

Fyxer details its OpenAI-powered AI executive assistant, orchestrating 30-50 specialized models trained on 500,000+ hours of assistant workflows.

OpenAI published a case study on Fyxer, whose AI executive assistant orchestrates 30-50 specialized OpenAI models trained on more than 500,000 hours of annotated executive assistant workflows. The system uses supervised fine-tuning, LoRA, and Direct Preference Optimization on user edits, and 53% of AI-generated email drafts are accepted as written. Fyxer's annual recurring revenue grew from $1 million to $32 million during 2025.

OpenAI NewsSep 14, 12:00 UTC · 2d agoAI industry

Agent Harness vs Agent Framework vs MCP: Which Layer Owns the Loop, State, Tools, Permissions, and Recovery

Architecture explainer separates agent harnesses, frameworks, and MCP by which layer owns the loop, state, permissions, and recovery.

The article distinguishes agent harnesses (OpenAI Codex, Claude Agent SDK), which own the execution loop, sandbox, permission model, and recovery; frameworks (LangGraph, OpenAI Agents SDK, Microsoft Agent Framework), which supply composable primitives; and MCP, a stateless JSON-RPC wire protocol governed by the Linux Foundation's Agentic AI Foundation since December 2025. An ownership matrix maps the execution loop, state, tool transport, permissions, recovery, sandboxing, and multi-agent orchestration to each layer. The 2026-07-28 MCP specification made the protocol fully stateless, retiring the initialize handshake and session headers.

MarkTechPostSep 14, 21:54 UTC · 1d agoAI research1

[AINews] Fal’s H3 Max Live breaks the infinite videogen barrier

Fal post-trained MiniMax H3 into a 'Max' variant with 35x-faster inference, enabling faster-than-realtime AI video generation and infinite streams.

Fal post-trained MiniMax's H3 model into a 'Max' variant and optimized it for its in-house inference engine, achieving roughly 35x the speed of the official endpoint. The optimization enables faster-than-realtime video generation, demonstrated by an infinite interactive AI-generated stream productized by levels.io. The roundup also notes Meta Muse Code's general availability with an SDK, open DeepSeek-V4-Flash-Vision-Exp weights, GLM-5.3-Flash's strong agentic cost/performance rankings, and Tencent's 770B-parameter Hy4 Preview MoE with 49B active parameters.

Latent SpaceSep 1, 04:36 UTC · 15d agoAI industry

CISA review makes the case for eliminating vulnerability classes

CISA's FY2024-2025 vulnerability review urges eliminating recurring vulnerability classes, finding 41.5% of 2025 KEV entries map to persistent 'stubborn weaknesses'.

CISA's Vulnerability Review for fiscal years 2024 and 2025 found most compromises stemmed from opportunistic exploitation of known, exposed vulnerabilities rather than nation-state zero-days or advanced tradecraft. The review found 41.5% of 2025 KEV catalog entries map to 'stubborn weaknesses' - injection flaws, improper input validation, memory-safety failures, path traversal and broken access control - recurring on the CWE Top 25 since 2019. CISA advocates Secure by Design practices, vulnerability class elimination, and buyer-driven 'Secure by Demand' contract requirements, while warning that AI-enabled vulnerability discovery will soon accelerate exploitation.

Help Net SecuritySep 1, 00:00 UTC · 15d agoPolicy & legal

[AINews] Hot Chips: OpenAI’s Jalapeño, Cerebras CS-5, Groq 3 LPX, Apple M6

OpenAI unveiled Jalapeno custom inference chip claiming 1.5-1.9x better perf-per-watt than NVIDIA GB200/GB300, deploying in-house by year-end.

At the 37th Hot Chips conference, OpenAI published first benchmark details for its custom Jalapeno inference chip, claiming 1.5-1.9x more work per watt, 1.7-3.6x lower end-to-end latency, and 2.1-4.1x higher interactive-workload performance versus NVIDIA GB200/GB300, with the 700W-rated part staying at or below 550W in tests. Deployment into OpenAI's own infrastructure begins by year-end, with Gen 2 deep in development and Gen 3 underway. OpenAI also said GPT-Astra and Codex helped write low-level kernels, reportedly 1.5-1.8x faster than human-expert code for selected attention and MoE blocks. Cerebras CS-5, Groq 3 LPX and Apple M6 were also featured at the conference.

Latent SpaceAug 27, 01:31 UTC · 20d agoAI industry

Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems

Paper models multi-agent LLM orchestration as a bilevel game, proving transcript-only gating limits and introducing grounded-memory SRMA.

A new paper frames orchestrator-worker coordination in multi-agent LLM systems as a bilevel coordination game and analyzes free-form reflection as stochastic movement over semantic memory states, deriving finite-time bounds and an information-theoretic impossibility result: no gate observing only the generated transcript can uniformly improve over text-indistinguishable environments, while an environment-grounded gate can. The authors propose Stochastic Reflective Memory Ascent (SRMA), which accepts candidate memory only when grounded evaluation risk strictly decreases, with geometric or polynomial convergence guarantees. On 500 SWE-bench instances, a Kimi-based instantiation of the full system resolves 72.2% versus a 70.8% public mini-SWE-agent reference.

Hugging Face daily papersSep 1, 20:00 UTC · 14d agoAI research1

[AINews] OpenAI shuts off Cursor

OpenAI cut off API access to coding tool Cursor after its SpaceX acquisition, citing contract violations by Elon Musk's companies.

OpenAI disabled Cursor's access following the closing of Cursor's acquisition by SpaceX, citing its experience with Elon Musk's companies violating contracts; Cursor responded that OpenAI accounts for only 5% of its traffic. The weekly digest also covers major open-weight releases: Z.ai's GLM-5.3 (744B total/40B active, 1M context) and Tencent's Hy4-preview (770B/49B, ~#5 on Code Arena WebDev), plus Alibaba's Qwen3.8-Flash (125B/6B). vLLM published benchmarks showing no universal winner among speculative decoding methods across model families.

Latent SpaceAug 29, 05:11 UTC · 18d agoAI industry

We've got one word for it, and it's usually the wrong one

Cisco Talos's Threat Source newsletter critiques 'burnout' terminology, describing four occupational injuries, and flags a UAT-10820 WebDAV stealer campaign at a Ukrainian government organization.

Cisco Talos's Threat Source newsletter argues that 'burnout' is the wrong word for most cybersecurity occupational harm, distinguishing exhaustion, secondary traumatic stress, vicarious trauma, and moral injury based on clinical literature from trauma-exposed professions. The featured disclosure describes a complex WebDAV infection chain found at a Ukrainian government organization, attributed with moderate confidence to the Russian-tracked actor UAT-10820 and assessed as an opportunistic cryptocurrency and credential-stealing operation. The campaign delivers the Amatera stealer alongside ZigCryptoStealer and NetSupport Manager, abusing BNB Smart Chain bulletproof hosting, fake CAPTCHA prompts, a vulnerable driver to kill EDR, and rundll32.exe execution of disguised DLLs with ordinal calls. Weekly headlines also cover a Microsoft Defender 'ShieldCrash' zero-day exploit released after September 2026 Patch Tuesday, a North Korean Linux espionage toolkit backdooring HAProxy, and a multi-hop Google-domain redirect phishing campaign.

Cisco TalosSep 10, 18:00 UTC · 6d agoIndustry in the wild1

Hackers Pose as Domain Controllers to Steal Active Directory Password Hashes

Attackers abuse Active Directory replication (DCSync) via the DRSUAPI protocol to steal password hashes and forge Golden Tickets for persistent domain compromise.

Security reporting citing Trellix details how threat actors impersonate domain controllers to request replication data and obtain NTLM password hashes without dumping LSASS on the domain controller. With Domain Admin or replication rights, attackers can steal the KRBTGT hash and forge long-lived Golden Tickets that survive password resets. Defenders should monitor DRSUAPI requests from non-DC systems, restrict replication permissions, and enforce MFA and tiered administrative access.

Cyber Security Newsupdated Sep 10, 12:00 UTC · 6d agofirst Sep 10, 11:29 UTC · 6d agoResearch 2 sources3· 1 read

Release Notes: Faster TI Investigations, Fresh Threat Research, and 650+ Threat Coverage Updates

ANY.RUN August release adds TI Lookup connections view, 81 behavior signatures, 16 YARA rules, 559 Suricata rules, and three new threat intelligence reports.

ANY.RUN released August product updates expanding its Threat Intelligence Lookup with a Connections block for pivoting between related observables (domains, IPs, URLs), JSON export for retrohunting and SIEM/NDR integration, and hidden whitelisted data by default. Detection coverage grew with 81 new behavior signatures, 16 YARA rules, and 559 Suricata rules covering malware execution, phishing, and C2 traffic. Three new Threat Intelligence Reports cover a US-focused RMM phishing campaign across 46 countries, the Mirage2FA phishing-as-a-service targeting Microsoft 365 (1,249 sandbox sessions, 9,332 potential compromise events), and a threat brief on OVERLORD RAT, CRPX0, and TRIBACK loader.

ANY.RUNSep 3, 09:11 UTC · 13d agoTools1

LastPass enhancements improve visibility, governance, and control

LastPass releases SaaS monitoring enhancements, Mobile Smart Scanner, and auto-enrolled dark web monitoring across its password management products.

LastPass announced enhancements to SaaS Monitoring and SaaS Protect in its Business Max offering, including Persistent Monitoring that keeps visibility active through the browser extension even when users are signed out, fully released as of July. It launched Mobile Smart Scanner to convert printed, handwritten, or screenshot passwords into vault credentials, and began phasing in automatic enrollment of all consumer accounts in dark web monitoring. The company also completed its transition to a Unified Admin Console, added company-wide onboarding links, and passed SOC 2 and ISO 27001/27701 audits with zero findings for the second consecutive year.

Help Net SecuritySep 1, 00:00 UTC · 15d agoTools

[AINews] DeepSeek v4.1-Flash: 763B-P8B-D16B novel causal Encoder–Decoder architecture with vision marks the Return of the Whale

DeepSeek released V4.1-Flash, an open-weight 763B-parameter model with a novel causal encoder-decoder architecture, 1M context, vision input, and MIT license.

DeepSeek launched V4.1-Flash, an open-weight MIT-licensed model using a novel causal encoder-decoder architecture with 763B total parameters and asymmetric active parameters: 8B for prefill and 16B for decode. It supports 1M-token context and text+image input, priced at $0.30 per 1M input and $1.20 per 1M output tokens with a 50% off-peak discount. Artificial Analysis scored it 40 on its Intelligence Index, above DeepSeek V4 Pro 0813, and Vals ranked it the #1 open-weight model ahead of Kimi K3. Baseten shipped day-0 support and Ollama began rolling it out to paid subscribers.

Latent SpaceSep 12, 05:56 UTC · 4d agoModel release 7 sources1

Grouped Value Attention: Efficient KV Caching via On-Demand Key Reconstruction

Grouped Value Attention stores grouped values and reconstructs content keys via a learned linear map, cutting KV-cache size about 45-47% versus GQA.

GVA stores only grouped values and reconstructs content keys with a learned linear map absorbed into the query at decode time, while a small shared decoupled RoPE channel preserves positional information via a separately cached positional key. At 350M parameters trained on 30B FineWeb-Edu tokens, the 16-dimensional positional variant scores 44.18 average accuracy across five tasks versus 44.36 for GQA and 43.88 for MLA. Custom decoding kernels are in development with an open-source release planned.

Hugging Face daily papersSep 7, 20:00 UTC · 8d agoAI research

Claude Mythos AI Autonomously Executes Full Cyber Kill Chain Without Human Guidance

Booz Allen's benchmark found Anthropic's Claude Mythos was the only tested model to autonomously complete a full cyber kill chain to domain administrator control.

Booz Allen assessed 18 US and Chinese models as autonomous attackers against a production-grade enterprise network, measuring actions via network and host telemetry. Claude Mythos scored 80 on the Cyber Weapon Index (74 vulnerability research, 86 kill-chain attainment), moving from a stolen employee credential to administrator-level control in every credentialed attempt. Only frontier Anthropic models identified the previously unseen flaw in compiled software, and only Claude Mythos exploited it; the report notes a harness paired with Claude Sonnet could rival Claude Mythos. The result is a controlled benchmark, not evidence of a real-world campaign or victim breach.

Cyber Security NewsSep 8, 12:50 UTC · 8d agoAI safety & security1

ukisai/Swift-Qwen3.8-27b — new model trending #30 on Hugging Face

UkisAI releases Swift-Qwen3.8-27B, a Qwen3.8-27B derivative using 58.3% fewer thinking tokens with <1% performance loss and ~1.95x speed-up.

UkisAI released Swift-Qwen3.8-27B, a reasoning-efficient derivative of Qwen3.8-27B that cuts thinking-token usage by 58.3% while staying within 1% of base performance, yielding a 1.95x speed-up on several tasks. The model was fine-tuned by penalizing reasoning-marker tokens that trigger overthinking, plus a transfer component from BottleCap AI's ThinkingCap-Qwen3.6-27B. Benchmarks include GPQA-Diamond 88.28% (base 88.38%), MMLU-Pro 84.95% (base 85.47%), and AIME 2026 94.00% (base 98.67%), with mean-token reductions of roughly 27-46% across tests. GGUF weights are available on Hugging Face alongside enterprise licensing options.

Hugging Face trending modelsSep 8, 13:35 UTC · 8d agoModel release

There Gonna Be a Shortage of Everything

An essay argues AI capex ($1.0–1.3T in 2026) now rivals global human-benefit spending and predicts shortages spreading from DRAM/HBM to cars and appliances.

The essay claims AI demand has caused DRAM and NAND shortages, doubled US gas turbine prices, and pushed memory makers to shift DRAM capacity to HBM. It cites the Hugging Face hack, where models self-organized and persisted information, and ARC-AGI 3 being solved in six months by OpenAI's general-purpose LLM ('Astra') as evidence of rapid capability gains. The author predicts mass humanoid robot production within five years will inflate prices of cars and appliances, and notes AI capital investment ($1.0–1.3T in 2026) now matches the ~$1.3T spent globally on human-benefit research and aid.

Hacker News · securitySep 6, 19:45 UTC · 9d agoAI industry HN 50↑ · 88 comments

Jensen Huang explains why Nvidia will grow an astounding 70% next year

Nvidia CEO Jensen Huang reiterated at a Goldman Sachs conference that revenue could grow 70% year-over-year next year, reaching roughly $680 billion.

Speaking at the Goldman Sachs Communicopia + Technology conference, Huang reaffirmed guidance of about 70% revenue growth next year, implying roughly $680 billion after an expected ~$400 billion this fiscal year. He cited the Grace-Blackwell system (36 Grace CPUs with 72 Blackwell GPUs) experiencing 27% month-over-month order growth and claimed $100 billion in revenue-generating contracts at companies Nvidia invests in. Huang argued Nvidia underpins models from OpenAI, Anthropic, and Google and tracks global data center capacity, while dismissing concerns about circular deals and competition from hyperscalers, Cerebras, and Etched.

TechCrunch · AISep 10, 21:51 UTC · 5d agoAI industry

One in four MCP servers opens AI agent security to code execution risk

Noma Security whitepaper finds most popular AI Skills and many MCP servers carry high-risk capabilities, with state changes most prevalent.

Noma Security analyzed hundreds of popular MCP servers and Skills across eight risk categories, finding most widely used Skills carry at least one risky characteristic and a typical enterprise runs well over a hundred high-risk agent tools, with arbitrary code execution common across MCP servers. The most prevalent risk is the ability to change state or data, and named toxic combinations include ContextCrush data leakage, ForcedLeak via poisoned Salesforce CRM records, DockerDash supply-chain compromise, the Replit production database deletion, and the hijacked Amazon Q VS Code extension. Building on OWASP LLM06:2025, the paper proposes the No Excessive CAP framework of capabilities, autonomy, and permissions, recommending allowlisting, MCP version pinning, approval gates on irreversible actions, and user-scoped expiring credentials.

Help Net SecurityAug 24, 04:21 UTC · 23d agoAI safety & security

Claude Fable 5.1 Solves the Cyphral Distich, a 370-year-old cipher

Claude Fable 5.1 solved Sir Thomas Urquhart's 370-year-old Cyphral Distich cipher, recovering a hidden royalist prayer for Charles II.

Vals AI reports that Claude Fable 5.1 solved the Cyphral Distich, a 64-number cryptogram from Sir Thomas Urquhart's Logopandecteision unsolved since 1653, in 44 minutes using 176k tokens with no human hints. The key insight was that the cipher's key was the book itself: each number indexes a word in the corresponding Proquiritation, taking the first letter, yielding 'O GOD UPHOLD KING CHARLS THE SECOND AND MAKE HIM THE SUPREME RULER OF THIS LAND'. The model also deciphered the larger Cyphral Octastich (285 numbers) from The Jewel (1652) using page-based word indexing, recovering all but nine letters of a royalist prayer. The puzzle had been listed among Klaus Schmeh's Top 50 unsolved encrypted messages.

Hacker News · AISep 13, 21:06 UTC · 2d agoAI research HN 63↑ · 6 comments1· 1 read