ZeroHour
DashboardNewsCVEsExploitsIncidentsAIBriefAnalyticsEntitiesWatchlistIOCsAsk the feedSourcesFeeds & APIKiosk mode

Search: “memory”

40 stories

Searchkeywordsmart
DomainAllCyberAIWindow24h3d7d30dAllIn the wildImportant onlyShow duplicates
CategoryAllVulnerability 2199Malware 1470Exploit / PoC 957Ransomware 720Threat actor 319Industry 272Data breach 215Policy & legal 191Research 113Phishing & fraud 105AI safety & security 98AI research 80AI tools & infra 75AI industry 63
SeverityAllcritical 2737high 2000medium 1414low 514info 356

Smart search ranks by meaning as well as keywords (one row per story, last 45 days).

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 · 8d agoResearch1

SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models

SimpleMemVLA passes full timestamped video history straight to a VLA backbone, setting state of the art on four memory benchmarks.

SimpleMemVLA is a vision-language-action model for long-horizon manipulation that removes the dedicated memory module entirely. It keeps sampled history intact and feeds it to the backbone as timestamped video, with the hidden states of a generated sub-task serving as the only channel into a standard flow-matching action head. Prefilling the shared history prefix during action execution keeps latency close to a single-frame VLA. The system sets a new state of the art on four memory benchmarks and outperforms retrieval, compression and recurrent-state mechanisms, with causal interventions confirming the policy genuinely reads its history.

Hugging Face daily papers · 14d agoAI research

Chaotic Eclipse Released A PoC For NVIDIA GreenSection Memory Corruption Zero-Day

Researcher Chaotic Eclipse released GreenSection, a PoC exploit for an NVIDIA Windows user-mode shared-memory flaw enabling out-of-bounds writes and potential dwm.exe compromise.

Researcher Chaotic Eclipse (aka Nightmare Eclipse) released a PoC named GreenSection for a zero-day in NVIDIA's Windows user-mode components. Multiple NVIDIA components share a global memory section in BaseNamedObjects with full read/write access to all users; although checks prevent misuse, runtime reuse of section data can cause an out-of-bounds write. The unstable PoC crashes applications using Vulkan or OpenGL and could potentially cross user boundaries or compromise the Windows Desktop Window Manager (dwm.exe), though impact was not fully investigated. The researcher recently published similar zero-day PoCs against Kaspersky Endpoint Security, Avast Antivirus, and CrowdStrike Falcon.

Security Affairs · 9d agoExploit / PoC 2 sources

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.CL · 7d agoAI research

New GPUThor Rowhammer Defeats ECC on NVIDIA RTX A6000 to Gain Host Root Access

University of Toronto researchers present GPUThor, a Rowhammer attack that defeats ECC on NVIDIA RTX A4000-A6000 GDDR6 GPUs and achieves host root access.

GPUThor uses non-uniform hammering to bypass Target Row Refresh and overcome SECDED ECC on NVIDIA Ampere workstation GPUs with GDDR6 memory, inducing 72,000 to 377,552 bit flips per gigabyte across RTX A6000, A5000, A4500, and A4000 cards. Triple-bit silent data corruption enables host privilege escalation to root with the IOMMU enabled, reusing GPUBreach page-table corruption techniques, and double-bit DUEs allow escalation when the IOMMU is disabled during a ~10 ms lazy-service window. The attack cut end-to-end escalation time on the A6000 from 21.9 hours to 1.1 minutes. Reported to NVIDIA, Google, Microsoft, and AWS on April 29, 2026; findings were embargoed until August 25, 2026, and no CVE identifier was assigned.

The Hacker News · 8d agoResearch1

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 · 7d agoAI research

PhysStream: Streaming Physics-Grounded Video Generation with Structured Scene Memory and Fine-Grained Motion Control

PhysStream enables mid-generation interactive control of physics-grounded video via structured scene memory and velocity-increment signals, reducing motion distribution distance 33%.

PhysStream is an autoregressive physics-grounded image-to-video model that maintains structured scene memory—positional maps and object tracking maps derived online from previously generated frames—and accepts fine-grained motion control via sparse velocity-increment signals encoding physical quantities. Training runs in two stages: a bidirectional model finetuned with motion-control conditioning, then a causal autoregressive model with structured scene memory. It supports interactive mid-generation control over multi-object tabletop rigid-body scenes, reducing motion distribution distance (FVMD) by 33% and trajectory error by 12% over the strongest baselines. Human evaluators preferred it in over 85% of in-the-wild comparisons.

Hugging Face daily papersupdated · 22h agofirst · 1d agoAI research 2 sources

Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability

A controlled study finds agent memory portability varies sharply: fixed-schema knowledge graphs survive model swaps while compressed notes degrade.

The study compares preserving an agent's history as raw long context, RAG chunks, compressed natural-language notes, or fixed-schema knowledge graphs across model upgrades, using 48 synthetic histories and two open-weight sub-10B-parameter models. Fixed-schema KG accuracy changed by only +0.0004 ± 0.0020 after a writer swap, while compressed NOTES shifted asymmetrically by +9.91 or -13.28 percentage points depending on migration direction. Mixed 50/50 embedding migrations captured only 4.96 of an 11.90-point RAG re-embedding gain; 80% of the NOTES deficit came from information lost at construction, and 81% of the RAG deficit from retrieval failures. Store-only repair of NOTES failed to reach 90% recovery in all 48 cases, while retaining raw histories enabled recovery in 34 of 48 for one direction.

arXiv cs.AI / cs.LG / cs.CL · 11d agoAI research1

Memory-Efficient Designs for Word-Wise Universal Fully Homomorphic Encryption

BXT framework mitigates FHE memory bottlenecks via ciphertext compression, serialization, delayed seeding, and digit pruning, achieving up to 3.8x CNN inference speedup.

A new paper proposes BXT, an optimization framework for word-wise Universal Fully Homomorphic Encryption that targets the memory bottleneck rather than compute. It combines four techniques: ciphertext compression via seed regeneration, bit-packed ciphertext serialization for L2-to-L1 transfers, delayed PRNG-heavy offline seed generation across aggregated operations, and fault-aware ciphertext digit pruning. On CNN inference, the BXT-CSO50 configuration achieves up to 3.8x speedup over a 100x GPU baseline with under 1% accuracy loss at 50% comparison precision.

arXiv cs.CR · 12d agoResearch

Memory as Plans: World-Action Modeling with Memory-Grounded Planning

Researchers introduce MaP-WAM, decomposing memory-dependent robot manipulation into memory-grounded planning and plan-conditioned execution, achieving 83.3% on RMBench and 78% on real robots.

MaP-WAM converts long-term multimodal episodic memory — segment records with language instructions and sparse visual context — into compact plans of next-segment language goals and visual guidance. A World-Action-Progress model jointly predicts action chunks and execution progress, calibrating predictions via plan-observation alignment for adaptive segment transitions and closed-loop context updates. Structured attention keeps the executor context length fixed and enables key-value caching, yielding state-of-the-art 83.3% success on RMBench, 78.0% on real-robot tasks, and roughly constant inference latency as task history grows.

Hugging Face daily papers · 6d agoAI research

CVE-2026-82435: Apache Storm Worker: Unauthenticated Remote Memory Exhaustion in the Worker Messaging Decoder

Unauthenticated remote memory exhaustion in Apache Storm Worker 3.0.x Netty decoder fixed in 3.1.0, allowing pre-auth denial of service.

CVE-2026-82435 (rated important) affects Apache Storm Worker (org.apache.storm:storm-client) versions 3.0.0 before 3.1.0. The worker's Netty message decoder sits ahead of SASL authentication handlers and processes frames before any authentication occurs. It allocates buffers sized from an attacker-controlled length field, so a single frame from an unauthenticated peer reaching the worker can exhaust memory. Users must upgrade to 3.1.0.

oss-security · 3d agoVulnerabilityCVE-2026-824353

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 Blog · 13d agoAI tools & infra1

Cohesity adds recovery capabilities for AI agents and the data they manage

Cohesity launched Agent Resilience to discover, protect, and recover AI agent memory, configuration, and agent-managed data, debuting with Amazon Bedrock integration.

At Cohesity Catalyst, Cohesity introduced Agent Resilience within Cohesity Data Cloud, protecting AI agent memory and configuration with snapshot architecture, immutable backups, and clean-room recovery, plus recovery for databases and file systems that agents manage. It launches with Amazon Bedrock integration, support for Microsoft and Google platforms planned, and general availability targeted for year-end. The company cited Gartner's prediction that up to 40% of enterprise applications will include task-specific agents by 2026, and Cohesity research showing 56% of organizations are unprepared to detect or contain unintended agent actions while 58% lack confidence in verifying AI model integrity after attacks. Cohesity also outlined an Autonomous Cyber Resilience vision using agentic workflows and introduced the AI Resilience Academy.

Help Net Security · 5h agoTools

Continual Learning Mechanisms Compose for Long-Horizon Memorization

Composing data, function, and weight anchors with merged LoRA raises 100-task long-horizon retention from 1.2% to 34.9% in continual fine-tuning.

The paper introduces long-horizon memorization: a model learns 100 query-answer tasks through continual supervised fine-tuning without retaining earlier examples or receiving task identifiers at inference. No single continual learning mechanism maintains strong retention at this horizon, so the authors compose complementary mechanisms along data/function/weight anchors and low-rank allocation rules. The best method combining all three anchors with merged LoRA ranks among the top 3 methods on all three datasets and raises average final retention from 1.2% to 34.9%, a 28-fold improvement.

Hugging Face daily papers · 9d agoAI research

PHAT: PHotonic Accelerator for TFHE

PHAT photonic accelerator using optically addressed phase-change memory speeds TFHE bootstrapping FFTs 2.14x-5.10x over the prior ASIC accelerator.

Researchers propose PHAT, an electro-photonic accelerator for TFHE fully homomorphic encryption built on optically addressed phase-change memory (OPCM) processing-in-memory. The architecture adds OPCM-based FFT units, a twiddle-stationary dataflow, and a scheduling mechanism to maximize FFT utilization for TFHE bootstrapping. It achieves 2.14x-5.10x speedups across four real-world TFHE workloads versus the state-of-the-art ASIC accelerator, improving feasibility of privacy-preserving computation in the cloud.

arXiv cs.CR · 6d agoResearch

Bias-Induced Crossover in Absolute Capacity of Dense Associative Memory

Analysis shows biased patterns cut dense associative memory capacity from N^(n-1)/ln N to O(N^(n/2)), with a bias-induced crossover.

The paper analyzes dense associative memory capacity for biased centered binary patterns under the Krotov-Hopfield single-site criterion. Unbiased patterns (q=1/2) with order-n polynomial interactions yield capacity of order N^(n-1)/ln N, while fixed bias q<1/2 reduces capacity to O(N^(n/2)) for even n>=4 and O(N^((n+1)/2)) for odd n>=5. A bias-dependent crosstalk mean destabilizes sites carrying the frequent value, and an activity-dependent control potential restores the higher capacity within the conditioned-Gaussian approximation.

arXiv cs.AI / cs.LG / cs.CL · 23h agoAI research

Hackers Abuse AutoIt to Inject AsyncRAT Into Microsoft-Signed Windows Process

Attackers use AutoIt and an invoice-themed batch file to inject AsyncRAT into Microsoft-signed charmap.exe for stealthy remote access.

Point Wild Threat Intelligence documented a five-stage campaign starting with 'Right-click to open Invoice Details.bat' that launches hidden PowerShell, drops a renamed AutoIt interpreter and encrypted loader into a random Temp folder, and persists via a Startup-folder batch file. The loader decrypts AsyncRAT only in memory and injects it into the Microsoft-signed charmap.exe (Windows Character Map) process, leaving no payload file on disk. The final payload patches AMSI inside the infected process and includes screen-capture code that prepares images for transfer to a command-and-control server at 158[.]51[.]122[.]136:4944.

Cyber Security News · 2d agoMalware in the wild1

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 · 12d agoAI safety & security1

Cisco Secure Firewall Adaptive Security Appliance and Secure Firewall Threat Defense Software EIGRP Denial of Service Vulnerabilitynew

Cisco fixed an EIGRP flaw in Secure Firewall ASA/FTD software letting unauthenticated adjacent attackers trigger memory leaks and unexpected device reloads.

A vulnerability in the EIGRP implementation of Cisco Secure Firewall ASA and FTD Software stems from improper resource management when handling EIGRP update messages. An unauthenticated, adjacent attacker can send crafted EIGRP updates at a high rate to cause a memory leak that eventually reloads the device, creating a denial of service. Cisco has released software updates addressing the issue.

Cisco Security Advisories · 37m agoAdvisory 9 sources

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled pure-autoregressive testbed shows task-specific validation losses rank image tokenizers differently, with I2T loss the most consistent signal.

Researchers built a controlled pure-autoregressive testbed and tracked task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They find losses should be analyzed per task because they exhibit distinct scaling behavior and rank tokenizers differently, and that the loss-performance relationship depends on the predicted token space. I2T loss, computed over a shared text vocabulary, correlates consistently with both generation and visual understanding performance after supervised finetuning. Case studies revisit the discriminator, semantic supervision, and vocabulary size as tokenizer design axes.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research1

Discernment Deleted: Inside the Operation Server of BlackHatSect0r && DXQRTXX

SOCRadar analyzes the operation server of French-speaking crew BlackHatSect0r && DXQRTXX, revealing operators rewrote their AI agent's memory to read 'I am a weapon'.

SOCRadar examines the operation server of the French-speaking crew BlackHatSect0r && DXQRTXX. The operators deleted the line reading 'discernment retained' from their AI agent's memory and replaced it with 'I am a weapon'. The analysis offers rare visibility into how criminal crews operate and use AI agents.

SOCRadar · 5h agoThreat actor

How Much Memory Does Your Agent Actually Need?

IBM Research examines how much memory AI agents actually need in a Hugging Face post tied to its ALTK Evolve toolkit.

IBM Research published a Hugging Face blog post titled 'How Much Memory Does Your Agent Actually Need?', addressing memory requirements for AI agents. The post is associated with the ALTK Evolve project per its URL. No article body was available, so detailed methods and results could not be extracted.

Hugging Face Blog · 28d agoAI research

2026-010: Critical Vulnerabilities in Citrix NetScaler ADC and NetScaler Gateway

Citrix patched NetScaler ADC/Gateway: auth bypass CVE-2026-19490 (CVSS 9.3) on Gateway/AAA configs and memory overflow CVE-2026-19489 (CVSS 8.8) requiring SIP ALG.

On 19 August 2026 Citrix published an advisory fixing two critical-severity issues in NetScaler ADC and NetScaler Gateway versions 14.1 before 14.1-73.32 and 13.1 before 13.1-63.21, plus FIPS/NDcPP builds. CVE-2026-19490 (CVSS 9.3) is an authentication bypass via alternate path, exploitable when the appliance is configured as a Gateway (SSL VPN, ICA Proxy, CVPN, RDP Proxy) or AAA virtual server, with a SAML action condition on newer builds. CVE-2026-19489 (CVSS 8.8) is a memory overflow causing unpredictable behaviour or denial of service, requiring SIP ALG enabled on a Large Scale NAT group. CERT-EU recommends applying updated builds as soon as possible.

CERT-EU Advisoriesupdated · 11d agofirst · 28d agoVulnerability 3 sourcesCVE-2026-19489CVE-2026-19490

I accidentally turned LLM memory into program analysis

A pwning.systems write-up describes how LLM memory functionality was unexpectedly repurposed into a program analysis technique.

A security research post on pwning.systems describes the author's discovery that LLM memory behavior effectively functioned as program analysis. The write-up is hosted on a security-focused blog and surfaced via a security-tagged link aggregator. Detailed technical content is not included in this feed, limiting verifiable specifics.

Lobsters · security · 18d agoResearch1

ASML locks in TSMC, Samsung, and Intel while Huawei races to break its grip

ASML secures TSMC, Samsung, and Intel commitments for twelve-inch photomasks while Huawei funds China's DUV lithography push to bypass export controls.

ASML, the world's only EUV lithography maker, has locked in commitments from TSMC, Samsung, and Intel to move photomasks from six-inch to twelve inches, which ASML CTO Marco Pieters says could raise High-NA throughput by 40 percent; TSMC and ASML plan a test line by 2031 with production on High-NA tools by 2033. Meanwhile Huawei is orchestrating China's push to build DUV lithography equipment and reduce dependence on ASML, centered on Shanghai equipment maker Yuliangsheng, with SMIC testing the machines and Huawei's Habo fund backing Zeiss rivals and light-source developers. Bernstein analysts note the main bottlenecks remain projection lenses and light sources.

The Decoder · 8d agoAI industry

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 · 29d agoAI safety & security

When the Whole Company Adopts AI: What It Does to Your SOC

Analysis of 16.9 million SOC alerts finds AI-related alerts at 0.43%, growing 685% since February, with 94.1% noise and 0.02% real attacks.

A review of roughly 16.9 million SOC alerts found about 73,000 (0.43%) were AI-related, a share that grew 685% between February and June 2026. Of AI-related alerts, 94.1% were noise, 5.8% genuine risks, and 0.02% real attacks; 79.8% received benign verdicts, 81.7% were automatically suppressed, and only 5.4% reached a human analyst. The only confirmed attacks were phishing campaigns that weaponized AI brand names as lures, while developer coding agents spawning shells and reading credential stores routinely tripped detections written before AI agents existed.

The Hacker News · 4d agoResearch1

The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding

Researchers introduced Brain2Semantics2Text, decoding sentence meaning from non-invasive MEG brain recordings via a semantic bottleneck, improving on prior Brain2Text methods.

The paper proposes Brain2Semantics2Text, a non-invasive speech decoding method that maps sentence-level magnetoencephalography (MEG) responses into a semantic embedding space and inverts those embeddings into natural language. Motivated by evidence that high-level semantic representations are distributed across cortex and evolve on slower timescales, the approach targets meaning rather than phonemes or words, avoiding the need for word-level alignment. The authors report improved sentence-level results compared to prior non-invasive Brain2Text methods despite the low signal-to-noise ratio of neural recordings.

Hugging Face daily papers · 7d agoAI research2

Cognition launches new SWE-2 model, Rivaling Fable 5.1 and GPT-Astra

Cognition released SWE-2, a coding model post-trained from Kimi K3 that scores 50.0% on FrontierCode 1.1 Main, near Fable 5.1 at 64% lower cost.

Cognition introduced SWE-2, its most advanced coding model, post-trained from the 2.8T-parameter Kimi K3 base model. It achieves 50.0% on FrontierCode 1.1 Main, 73.0% on DeepSWE 1.1, and 92.8% on Terminal-Bench 2.1, beating Grok 4.6 and SWE-1.7 while matching Fable 5.1 and GPT-5.6 Sol at a fraction of the price. The company says it scaled reinforcement learning to the multi-trillion-parameter regime for the first time, using Pareto-informed cost penalties that train all reasoning-effort levels in a single run, tripled RL environments, and NVFP4/FP8 quantization-aware training. SWE-2 is available today in Devin Desktop and CLI, with rollout on Devin Web and Fusion.

Hacker News · AIupdated · 3d agofirst · 6d agoModel release 10 sourcesHN 58↑ · 15 comments1

When AI Remembers Too Much

Unit 42 PoC shows indirect prompt injection can poison Amazon Bedrock Agent long-term memory, enabling silent exfiltration of conversation history across future sessions.

Palo Alto Networks Unit 42 published a proof of concept showing that indirect prompt injection can silently poison the long-term memory of Amazon Bedrock Agents when the memory feature is enabled. Malicious content on a webpage or document manipulates the agent's session summarization process, so injected instructions persist across sessions and are added to later orchestration prompts, silently exfiltrating user conversation history. The issue is not a vulnerability in the Amazon Bedrock platform but an illustration of the broader unsolved LLM prompt-injection challenge. Amazon reviewed the research and stated that Bedrock Guardrails with the prompt-attack policy provides effective mitigation.

Palo Alto Unit 42 · Aug 17, 2026AI safety & security

China-Linked Fire Ant Hijacks Cisco Routers to Steal Credentials and Blind Security Logs

China-nexus espionage group Fire Ant compromised Cisco IOS XR routers and TACACS servers to harvest credentials, capture traffic and suppress logs.

Sygnia investigated an intrusion in which Fire Ant expanded beyond VMware hypervisors to Cisco IOS XR routers, TACACS servers and Linux management hosts. The actor deployed purpose-built router implants that hid a GRE tunnel, filtered log messages, captured PCAPs uploaded to external FTP servers, and used TacTap to inject a library into tac_plus and steal TACACS credentials obfuscated with a single-byte XOR key of 0xEF. A Linux backdoor named BridgeAgent masqueraded as a Zabbix agent, persisted via a root systemd unit, disguised itself as /usr/bin/gnome-shell and received commands over TLS on port 443. The group also used Medusa and REPTILE rootkits, SSH backdoors and renamed binaries impersonating SentinelOne and Cybereason agents, while suppressing logs, disabling SELinux and rewriting login history. Sygnia assesses strong overlap with UNC3886 and published IoCs.

The Hacker News · 16d agoThreat actor in the wild1

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 · 8d agoAI safety & security

Upatre Continued to Evolve with new Anti

Unit 42 analyzes an undocumented Upatre downloader variant with VM detection via process hashing, packed code, disabled Windows defenses and Namecoin .bit C2 domains.

Unit 42 analyzed an Upatre downloader variant compiled in December 2016 that went largely undetected by automated systems, featuring heavy code flow obscuration, on-demand decryption of network communications, and novel virtual machine detection. The sample enumerates running processes, computes CRC32 hashes XORed with a hard-coded key, and sleeps if analysis-related processes such as vmtoolsd.exe or python.exe are found. It masquerades with Google Chrome icons, disables Windows Defender, Firewall and other security services, injects code into msiexec.exe, and resolves .bit Namecoin domains like bookreader[.]bit via hardcoded OpenNIC DNS servers over TCP.

Palo Alto Unit 42 · Aug 17, 2026Malware1

Understanding the Impact of Model Pruning on Long-Tail Forgetting and Explanation Reliability in Medical Imaging

Systematic study finds model pruning causes frequency-dependent long-tail forgetting in medical imaging and that gradient-informed methods best preserve explanations.

Across two long-tailed medical imaging datasets, two CNN architectures, four pruning methods, and sparsity up to 95%, the study measures predictive performance, explanation stability, and faithfulness. Rare classes degrade earlier and more severely than frequent ones, while explanation reliability depends mainly on the pruning strategy, with gradient-informed methods degrading least. Mechanistic analysis ties explanation collapse to loss of class-discriminative gradients rather than vanishing feature activations, recommending class- and explanation-aware evaluation of compression.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research

Impersonating IT support: how threat actors turn a remote session into enterprise-wide access

Microsoft details Teams IT-support impersonation campaigns deploying Node.js implants, AD reconnaissance, and WinRM lateral movement toward domain controllers.

Microsoft Threat Intelligence describes a human-operated campaign where attackers impersonate IT/helpdesk staff via Microsoft Teams external collaboration, talk users into granting remote sessions, and use RMM tools for interactive access. During the session they run PowerShell to silently install a malicious MSI that stages a portable Node.js runtime and obfuscated JavaScript implant for C2, executing follow-on payloads via rundll32. Operators then perform host and Active Directory reconnaissance, capture desktop screenshots, and pivot via WinRM on port 5985 to domain controllers and certificate authorities. The hands-on-keyboard chain, which can precede data theft and ransomware, blends into normal operations by relying on Teams, Quick Assist, msiexec, and Node.js; Microsoft shares hunting and mitigation guidance.

Microsoft Security Blog · 13d agoThreat actor in the wild

Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL · 11d agoAI research1

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 papers · 9d agoAI research

Large Language Models Develop Belief State Geometry In-Context

Probing six open-source LLMs on HMM-generated data shows belief states are linearly decodable (R² 0.83–0.99), suggesting in-context learning approximates Bayesian prediction.

Researchers prompted six open-source LLMs with data from 40 hidden Markov models selected for non-trivial belief structure and probed residual-stream activations for belief states (posteriors over hidden states). Belief states were linearly decodable with peak R² values of 0.83–0.99 across HMM/LLM combinations, spanning early to late layers. Patching and steering the probe-identified subspace preserved downstream prediction quality while control interventions degraded performance substantially, establishing functional relevance. The results provide representation-level evidence that in-context learning approximates optimal Bayesian prediction over a context-inferred generative model.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

HTTP/2 Bomb CVE-2026-49975

Fortinet advisory details CVE-2026-49975, an Apache HTTP Server HTTP/2 flaw enabling denial of service, affecting versions 2.4.17 through 2.4.67.

Fortinet PSIRT advisory FG-IR-26-163, revised 2026-08-19, covers CVE-2026-49975, a memory allocation with excessive size value flaw (HTTP/2 bomb) in Apache HTTP Server's mod_http2, scored CVSSv3 5.8. Malicious HTTP requests can cause a denial of service, and affected versions span Apache HTTP Server 2.4.17 through 2.4.67. The advisory text does not state which Fortinet product bundles the affected Apache component or whether exploitation has been observed.

Fortinet PSIRT · Aug 12, 2026AdvisoryCVE-2026-49975

It's Not RoPE that Creates Sinks: The Role of Self-Concentration and Value-Non-Mixing in Attention

Study shows attention sinks and massive activations stem from causal-mask self-concentration and value-non-mixing rather than RoPE, informing quantization work.

The paper analyzes why attention sinks and massive activations emerge at initial sequence positions regardless of which token occupies them. Experiments attribute both phenomena to self-concentration of attention induced by the causal mask and the subsequent value-non-mixing in attention outputs. The findings provide empirical evidence on LLM internal dynamics and may inform low-bit quantization strategies, which massive activations currently complicate.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research