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Palo Alto Networks positions Cortex XSIAM as a purpose-built platform to simplify security operations and speed incident remediation.
The Unit 42 blog page is a product category archive for Cortex XSIAM. The platform is marketed as purpose-built for SecOps transformation, simplifying security operations and accelerating incident remediation against current and future threats. No research findings, incidents, or vulnerabilities are discussed.
CISA warns CVE-2026-77477 (CVSS 4.6) lets local attackers hijack a privileged console during OPC UA LDS installation below version 1.04.420.
CISA published ICSA-26-246-01 for CVE-2026-77477, CWE-250 execution with unnecessary privileges in OPC Foundation UA-LDS-Installers below 1.04.420. An attacker able to launch the installer with elevated privileges and access the keyboard and display can intercept a high-privilege console window during installation and run arbitrary commands. CVSS 3.1 score is 4.6, the issue is not remotely exploitable, and no public exploitation has been reported. Lukas Schumaker of Rockwell Automation reported the flaw to OPC Foundation.
Unit 42 archive page for Code to Cloud platform product-category articles; no new research or announcements.
This Unit 42 archive page lists product-category posts about Palo Alto Networks' Code to Cloud platform, which secures public cloud environments, SaaS applications and remote locations. It contains no new research, incidents, or announcements.
Chain-of-Self-Questioning prompting cuts LLM wrong-answer commitments 32% relative while raising answered accuracy, holding across eleven model families.
The paper introduces Chain-of-Self-Questioning (CoSQ), a prompt-only framework that makes LLM answer commitment conditional on an explicit assessment of the information required to answer. On an 817-item TruthfulQA multiple-choice set, Grounded-CoSQ at τ=0.90 reduced mean unconditional wrong-commitment rate from 13.1% under chain-of-thought to 8.9% (a 32.1% relative reduction), while raising answered accuracy from 86.9% to 89.7% at 87.6% coverage. Improvements held across eleven open-weight and hosted model families and at every evaluated threshold, with convergent evidence from a Natural Questions short-answer evaluation.
Unit 42 archive page for Prisma Cloud product-category articles; contains no new research or announcements.
This Unit 42 archive page lists product-category posts about Prisma Cloud, Palo Alto Networks' cloud security platform for public clouds, SaaS applications and remote users. It contains no new research, incidents, or product announcements.
Researchers introduce NOAH, a generative time-aware transformer trained on 559 million MIMIC clinical events to model and forecast patient trajectories.
NOAH is a task-agnostic, time-aware generative transformer designed to represent and forecast the full multimodal patient journey across medical images, time-series signals, categorical events, and clinical text. It was trained on over 559 million clinical events from 431,000 hospital visits covering 299,000 patients in the MIMIC dataset family. The architecture combines bidirectional time integration with a variational latent space to capture continuous patient state evolution and clinical stochasticity. NOAH supports autoregressive forecasting with time control, zero-shot classification, and counterfactual intervention simulation, with evaluations on 15 ICD chapters, 29 comorbidities, and time-to-event prediction.
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%).
Interpretability study traces how Qwen, Llama, and Gemma route query information and internal knowledge across layers when answering questions.
Researchers used layerwise interventions on hidden states to separate query-routing signals from target knowledge in language models, testing Qwen, Llama, and Gemma on country-continent questions with varied answer types. A pair-conditioned request direction strengthens before interventions alter downstream knowledge, opening a causal window while answer-supporting content is still forming. Trajectories differ by model: Gemma shows a partially overlapping mid-layer routing profile, while Llama has no sustained routing-effect window under the same gates.
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.
Palo Alto Networks Unit 42 describes its Deep and Dark Web service monitoring underground forums for compromised credentials and leaked data.
Unit 42's Deep and Dark Web Service page describes monitoring underground forums to identify compromised credentials and leaked data. Analysts investigate suspicious content, escalate prioritized findings, and deliver actionable intelligence to customer SOCs. The page is a service description rather than a news report or incident disclosure.
Researchers introduce NOAH, a time-aware generative transformer trained on 559 million MIMIC clinical events to forecast multimodal patient trajectories.
NOAH is a task-agnostic, time-aware generative transformer trained on over 559 million clinical events from 431,000 hospital visits by 299,000 patients across the MIMIC dataset family. It uses bidirectional time integration and a variational latent space to model the stochastic evolution of patient states, natively processing medical images, time-series signals, categorical events, and structured or unstructured clinical records. The model supports autoregressive forecasting with optional time control, zero-shot classification, and counterfactual intervention simulation, with strong probing performance across clinical outcomes, 15 ICD chapters, and 29 comorbidities.
A review paper frames on-policy self-distillation collapse as governed by three levers: token weighting, privileged information, and guidance decay.
The paper critically reviews On-Policy Self-Distillation (OPSD), where a language model trains on its own generations scored token-by-token by a teacher conditioned on privileged information such as reference solutions or environment feedback. It identifies collapse, the progressive narrowing of producible reasoning paths, as the dominant failure mode and analyzes it through three levers: signal weighting, the nature of privileged information, and teacher dynamics. The review is restricted to mathematical reasoning, reports no new experiments, and offers a shared vocabulary separating settled findings from disputed ones.
Palo Alto Networks Unit 42 hosts a product archive page for its Cloud-Delivered Security Services subscription portfolio.
The page is a product category archive on the Unit 42 blog with no article text available. Cloud-Delivered Security Services is Palo Alto Networks' bundle of cloud-delivered network and threat prevention services. No research findings, incidents, or vulnerabilities are described.
Opinion piece argues multi-domain login redirects make legitimate flows indistinguishable from phishing, urging organizations to standardize on one recognizable root domain.
The author shows a real-world login flow that chains through numerous third-party domains (identity providers, cloud auth endpoints, OAuth APIs, 2FA pages), none on the company's own domain, so credentials and 2FA prompts are trivially impersonable. The piece argues URLs are inherently confusing because the security-relevant second-level domain sits mid-string, and that users trained to ignore URLs cannot distinguish legitimate pages from scams. It proposes RFC 2119-style requirements: single recognizable root domain, internal services on its subdomains, emailed or SMS links on that domain, and local redirects instead of third-party hosted pages.
Deliberative diagnostic shows all five tested frontier LLMs misrepresent human belief shifts after arguments, with GPT-5.1 reversing on outgroup questions.
The Deliberative Polling Diagnostic Framework compares human and LLM persona belief shifts after identical informational interventions, using data from America in One Room (526 personas, 72 questions). All five frontier models tested failed uniquely: GPT-5.1 exhibited partisan reversal (80% on outgroup vs 26% on policy questions), Gemini 2.0 Flash, Claude Sonnet 4.5 and Llama 3.3 70B overshot at 5-7x human magnitude, and DeepSeek V3 showed near-zero change (rigidity). The authors term the underlying signature 'self-sycophancy', conformity to the model's internal persona stereotype rather than reasoning from provided information.
Formal Dolev-Yao analysis shows plain Certificate Transparency requires an honest log, SCT Auditing removes that assumption, and Gossiping does not.
The paper analyzes accountability in Certificate Transparency and its SCT Auditing and Gossiping extensions in the Dolev-Yao model, starting from a vanilla PKI. It finds plain CT provides accountability only under the assumption of an honest log. The SCT Auditing extension can eliminate that assumption, while the Gossiping extension cannot. CT is supported by all major browsers and obliges Certificate Authorities to record issued certificates in public, monitored logs.
A self-distillation safety framework tunes narrow-boundary refusals in Qwen3-8B, raising target-domain refusal to 84.75% while cutting over-refusal from 15.20% to 5.20%.
The paper formulates narrow-boundary safety, where deployments need refusals within specific topics rather than whole subjects, and proposes an offline self-generated framework with controlled topic generation, escalating retries, and harmful-benign boundary pairs. On political persuasion with Qwen3-8B, the method raised target-domain refusal from 9.47% to 84.75% and cut the mean unsafe-response rate across three broader benchmarks from 26.26% to 0.14%. Verified target-model responses reduced over-refusal from 15.20% to 5.20%, and boundary-pair data cut comply-side over-refusal on held-out pairs from 32.94% to 4.16%. Results show data composition controls the safety-usability trade-off and alignment should be evaluated on both sides of the refusal boundary.
The Hunter's Ledger tracked campaign UTA-2026-024 using Sliver C2, Domain Admin account creation, and Ethereum-based C2 rotation to compromise a US organization's Windows domain.
The Hunter's Ledger tracked an intrusion at one unnamed US organization as UTA-2026-024, staged from exposed server 193.233.202.17 with a Sliver beacon. Operators created a non-expiring Domain Admin account, enabled RDP with NLA disabled, dumped SAM, SYSTEM and SECURITY hives plus LSASS memory, and disabled eight endpoint protection services. A Node.js implant resolved its C2 server from an Ethereum smart contract that rotated domains five times in five months, while SYSTEM scheduled tasks with backdated dates and DNS allowlist manipulation provided persistence. The infrastructure ties to a confirmed ransomware incident, but no encryptor deployment was proven in this intrusion.
Palo Alto Networks publishes a product category page for Cortex Cloud, an AI and automation platform for cloud and SOC workflows.
This item is a product category archive page for Palo Alto Networks' Cortex Cloud, describing it as an AI and automation-driven platform spanning code to cloud to SOC. The page contains no new research, incident, or product release details beyond marketing language about prioritizing risks and accelerating remediation.
Study finds zero-shot time-series foundation models underperform on CGM forecasting; fine-tuned Chronos-Bolt cuts RMSE up to 18.4% and dietary context adds signal.
The paper evaluates time-series foundation models for continuous glucose monitoring forecasting across eight public datasets covering Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol, zero-shot foundation models did not consistently outperform baselines like Elastic Net and PatchTST, but lightweight fine-tuning did, with fine-tuned Chronos-Bolt reducing RMSE by 6.5%-18.4% in the T1D cohort and 8.6%-18.2% in the non-diabetes/T2D cohort. A residual-based fusion framework adding dietary context from CGMacros reduced overall RMSE by about 3% and postprandial RMSE by about 15% versus CGM-only baselines.
Evaluation of twelve LLMs on 222 clinical questions shows verbatim quotes rarely substantiate claims; claude-opus-5 fully substantiates only 37.1%.
The authors build a standardized harness over four clinical practice guidelines and evaluate twelve LLMs on 222 synthetic clinical questions, measuring citation attachment, verbatim quote production, and claim substantiation. Most models attach verbatim quotes to over 90% of claims from prompting alone, though lightweight models like claude-haiku-4.5 struggle. Quotes frequently fail to substantiate claims: claude-opus-5 quotes 98.0% of claims but fully substantiates only 37.1%, exposing a capability gap for verifiable clinical QA.
Controlled mid-training experiments on Qwen3-8B-Base find each domain has a 10-40% coverage optimum and domain gaps survive alignment SFT.
Using Qwen3-8B-Base (with a 4B replication) across five semantically rule-disjoint KOR-Bench domains, the authors train 30 data allocations spanning the five-domain simplex at five seeds each. All five domains show interior optima in the moderate 10-40% coverage band, and domain gaps persist after a fixed-budget compensatory SFT pass, which raises 116/120 cells yet bridges 0/240 pairs at a 5% threshold. Zero coverage collapses mid-training-only accuracy, though a FineWeb-Edu-only control shows the collapse is partly generic drift. The results argue mid-training data composition requires principled design rather than reliance on later alignment.
Vendor product-category page for Palo Alto Unit 42 IoT security content, published with no article text available.
The URL points to Palo Alto Unit 42's 'IoT Security' product category listing rather than a research article. No article body was available, so no incident, vulnerability, or research findings can be extracted from this item.
A developer released a browser-based tool that visualizes which past tokens influence each LLM output token using aggregated, value-weighted attention scores.
A Show HN project presents a React application built on Transformers.js that renders per-token attention influence by aggregating attention weights scaled by value-vector magnitudes across all attention heads and layers. To expose internal tensors, the author instrumented the ONNX computation graph, hosted a modified model on Hugging Face, and pre-generated prompts to avoid long model downloads in the browser. Demos with a 600-million-parameter model show how verbatim copying draws heavily on source tokens and how single outputs blend information from multiple phrases.
Atlas delivers zero-knowledge proofs for HNSW semantic search, verifying RAG retrieval in under a second on SIFT1M and 2.0 seconds at 100M vectors.
Atlas lets a search provider prove that a query was answered correctly against a committed HNSW index without revealing the index, addressing provider deviations like truncation or bias. It combines offline preprocessing, a fixed-size-state restructuring of HNSW with a correctness proof, and timestep-tagged batching of per-step arguments. The system proves queries in under a second on SIFT1M and 2.0 seconds at 100 million vectors while preserving plaintext HNSW recall, and proven retrieval maintains end-to-end RAG answer quality at lower cost than prior verifiable retrieval systems.
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.
Unit 42 detected 577,000 landing URL incidents in Q1 2022, with an old web skimmer family still actively stealing payment card data.
Palo Alto Unit 42 detected 577,275 landing URL incidents (116,643 unique) and 2,043,862 malicious host URL incidents (180,370 unique) between January and March 2022. Web threat volumes declined after the November 2021 holiday peak, but an old web skimmer family remained active. Business and economy sites overtook personal sites as the most common apparently benign entry points. Most malicious domains geolocated to the United States, Germany, and Russia, though proxy servers and VPNs obscure true locations.
IBM Research's ALTK-EVOLVE-SLDD blog post claims ACE-style LLM analysis using substantially fewer tokens.
IBM Research published a Hugging Face blog post titled 'Thinking of ACE? We Can Do It with Fewer Tokens' describing ALTK-EVOLVE-SLDD. The method targets reproducing ACE (attribution-based confidence estimation) capabilities in LLMs while consuming far fewer tokens. No article text was available, so details beyond the title are limited.