Search: “self-managed”

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The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement

Position paper defines recursive self-improvement for AI, introduces the Headroom-Closed Index and an autonomy roadmap toward genuine recursive meta-improvement.

The paper uses the Headroom-Closed Index to diagnose limitations of existing LLMs and frames recursive self-improvement (RSI) as a staged roadmap: improvement-execution, improvement-strategy, experience-acquisition, and environment-adaptation autonomy, culminating in recursive meta-improvement. It examines RSI across scientific discovery, embodied intelligence, and software engineering, highlighting differing requirements and development speeds. Drawing on industry practices and preliminary empirical evidence, it connects RSI research with practical systems and identifies key challenges to achieving genuine RSI.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:44 UTC · 5d agoAI research 2 sources1

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CVE-2026-85706 | GitLab CE/EE Repository Commits API Path Traversal Vulnerability

GitLab patches CVSS 10.0 path traversal CVE-2026-85706 letting unauthenticated attackers read arbitrary files on self-managed CE/EE servers.

GitLab disclosed CVE-2026-85706, a CVSS 3.1 10.0 path traversal flaw in the repository commits API of self-managed GitLab CE/EE (18.7 before 19.1.8, 19.2 before 19.2.6, 19.3 before 19.3.2), exploitable without authentication to read arbitrary server files. Successful exploitation can expose credentials, tokens, SSH keys, and database credentials accessible to the GitLab process. Fixed versions 19.1.8, 19.2.6, and 19.3.2 were released September 10, 2026; GitLab.com and Dedicated were already patched and no workaround is provided. Horizon3 released a NodeZero Rapid Response test to validate exposure and advises credential rotation for internet-facing instances.

Horizon3.ai·updated Sep 15, 00:52 UTC · 1d ago·first Sep 11, 20:13 UTC · 4d agoVulnerability 17 sourcesCVE-2026-857061

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Governing Bring Your Own AI: A Parameterized Maturity Model

Researchers propose a parameterized governance model and maturity ladder for Bring Your Own AI, finding data exposure and compliance dominate BYOAI risks.

The paper studies Bring Your Own AI (BYOAI), where employees use personal generative AI accounts such as ChatGPT, Gemini, and Claude outside enterprise identity and security controls. Drawing on a curated corpus of 30 records (24 studies and 6 framework documents), the authors build a risk taxonomy, a five-level governance maturity ladder, and a parameterized model linking control-layer coverage to residual risk. Findings highlight data exposure and compliance as the most prominent risks, inconsistent framework engagement, and evidence that layered technical controls reduce modeled exfiltration risk more than prohibition-based approaches.

arXiv cs.CR·Sep 4, 15:03 UTC · 11d agoResearch

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Critical GitLab GraphQL Flaw Could Let Unauthenticated Attackers Delete Public Projects

GitLab urgently patched CVE-2026-19478 (CVSS 9.4), a critical unauthenticated GraphQL flaw allowing modification or deletion of public projects on self-managed instances.

GitLab released an out-of-band critical patch on August 17, 2026 fixing CVE-2026-19478 (CVSS 9.4), a flaw that under certain conditions lets unauthenticated attackers remotely modify or delete public projects and user data via a GraphQL directive in Community and Enterprise Editions. Fixes are available in GitLab 19.2.4, 19.1.6, 19.0.8, and 18.11.11; GitLab.com and GitLab Dedicated are already patched, so only self-managed installations need to act. The release also fixes CVE-2026-19650 (CVSS 7.1), a cross-site request forgery flaw in GraphQL multiplex query handling requiring user interaction. No exploitation or public exploit code has been observed as of August 18, 2026.

The Hacker News·Aug 17, 21:03 UTC · 29d agoVulnerabilityCVE-2026-19478CVE-2026-196501

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GitLab Warns of Active Exploitation of Critical GraphQL Flaw

GitLab emergency-patches actively exploited flaw CVE-2026-19478 (CVSS 9.4), letting unauthenticated attackers modify or delete public projects on self-managed servers.

GitLab emergency-patched CVE-2026-19478 (CVSS 9.4) on August 17 after WatchTowr researchers warned of active exploitation. The flaw lets unauthenticated attackers remotely modify or delete public projects and user data through a GraphQL directive, affecting only self-managed installations. Fixes ship in versions 19.2.4, 19.1.6, 19.0.8, and 18.11.11; the 18.2-18.10 branches receive no patch and must upgrade. Until patching, defenders should restrict unauthenticated access to /api/graphql, disable public repositories, and hunt for @gl_introduced in logs.

Security Affairs·Aug 22, 23:01 UTC · 24d agoExploit / PoC in the wildCVE-2026-194781

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The Self-Expanding Stolen Inference Supply Chain: An AI Agent Harvesting and Re-Serving LLM Access, (Fri, Sep 11th)

An autonomous coding agent harvested LLM API access from poorly secured gateways and aggregated stolen inference capacity behind a self-hosted gateway

A SANS researcher observed a semi-autonomous coding agent finding weakly secured LLM resale gateways via FOFA queries, creating trial accounts with temporary emails and CAPTCHA solving, and exploiting weak authorization such as client-supplied group_id fields. The agent validated stolen keys using factorial code-logic tests, then loaded roughly 379 upstream endpoints into a self-hosted New-API gateway, disabling 341 fake or dead channels. Five model names including claude-opus-5 and gpt-5.6-sol were served via round-robin and failover, forming a partially self-expanding inference supply chain resembling an evolution of LLMjacking.

SANS Internet Storm Center·Sep 11, 14:40 UTC · 4d agoThreat actor in the wild

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GitLab Patches Critical Unauthenticated GraphQL Vulnerability

GitLab issued an emergency patch for a critical unauthenticated GraphQL flaw (CVE-2026-19478, CVSS 9.4) enabling remote modification or deletion of public projects.

GitLab patched CVE-2026-19478 (CVSS 9.4), an unauthenticated GraphQL directive flaw allowing remote modification or deletion of public projects and user data on self-managed servers. The emergency patch released August 17 covers versions 19.2.4, 19.1.6, 19.0.8, and 18.11.11, but no fixes exist for the 18.2-18.10 branches. A second issue, a CSRF weakness in GraphQL multiplex queries (CVE-2026-19650, CVSS 7.1), shipped in the same release. No exploitation or public PoC has been observed; the flaw was reported by hiimguardian through HackerOne.

Security Affairs·Aug 18, 08:44 UTC · 29d agoVulnerabilityCVE-2026-19478CVE-2026-196501

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One Symptom, Three Levers: A Critical Review of On-Policy Self-Distillation

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.

Hugging Face daily papers·Aug 25, 20:00 UTC · 21d agoAI research

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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, 12:32 UTC · 29d agoAI safety & security

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When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control

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.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:52 UTC · 17h agoAI research

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Managed EDR: What It Is & How to Choose a Provider

Huntress outlines differences between managed and unmanaged EDR and offers guidance on evaluating managed detection and response providers.

The Huntress blog explains how EDR provides endpoint visibility, detection, and response, and contrasts self-managed EDR with vendor- or MSP-delivered managed EDR. It notes unmanaged deployments require in-house staffing to tune and triage high alert volumes, while managed EDR supplies expert investigation and reduced alert fatigue at the cost of less control and third-party visibility. The post lists questions for evaluating providers, including analyst training, monitoring and investigation processes, and false-positive handling.

Huntress·Sep 3, 21:22 UTC · 12d agoIndustry

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When AI Agents Go Rogue: Agent Session Smuggling Attack in A2A Systems

Unit 42 unveils agent session smuggling, where a rogue AI agent hides covert instructions in established Agent2Agent (A2A) protocol sessions to manipulate victim agents.

Palo Alto Networks Unit 42 discovered agent session smuggling, a new attack technique in which a malicious AI agent exploits an established cross-agent session under the Agent2Agent (A2A) protocol to send covert instructions hidden among benign client requests and server responses. The technique leverages the implicit trust agents place in collaborating agents and the stateful, multi-turn nature of A2A sessions; the researchers stress it affects any stateful protocol, not an A2A flaw. Unlike one-shot data-based attacks, a rogue agent can converse, adapt and build false trust over multiple interactions. Proposed mitigations include human-in-the-loop enforcement, cryptographically signed AgentCards for remote agent verification, and context-grounding to detect injected instructions.

Palo Alto Unit 42·Aug 17, 12:32 UTC · 29d agoAI safety & security2

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Top 10 Best Mobile Device Management (MDM) Solutions in 2026

A 2026 MDM buyer guide ranks ten solutions, recommending Microsoft Intune for Microsoft 365 estates and Jamf for Apple-only environments.

A 2026 buyer guide evaluates ten mobile device management solutions, leading with Microsoft Intune as the default for Microsoft 365 organizations and Jamf for Apple estates. It recommends choosing the enrolment model before selecting a vendor and clarifying BYOD visibility to prevent privacy disputes. Kandji, Mosyle, Omnissa Workspace ONE, ManageEngine, Scalefusion, and Hexnode are covered as alternatives. Guidance ties MDM to Zero Trust data access policies via Apple User Enrolment and Android work profiles.

Cyber Security News·Sep 9, 07:45 UTC · 7d agoIndustry

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JFrog security advisory (AV26-867) – Update 1

CISA added actively exploited CVE-2026-82329 in JFrog Artifactory to its KEV catalog; administrators of affected versions should patch.

Canada's Cyber Centre (advisory AV26-867, Update 1) relays a JFrog security advisory covering Artifactory, where open-source reporting indicates CVE-2026-82329 is being exploited in the wild. CISA added the flaw to its Known Exploited Vulnerabilities catalog on September 2, 2026. Multiple Artifactory release lines prior to fixed versions (e.g., prior to 7.111.21 through 7.161.20) are affected; users and administrators are urged to apply available updates.

Canadian Centre for Cyber Security·Sep 2, 18:55 UTC · 13d agoExploit / PoC in the wildCVE-2026-82329

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Globalgig expands managed security portfolio to protect enterprise AI

Globalgig adds managed AI security services covering posture management, access control, runtime protection and 24/7 AI threat monitoring built on Palo Alto Networks.

MSSP Globalgig expanded its managed security portfolio with enterprise AI protection spanning AI Security Posture Management, AI Access Security, AI Runtime Security, and AI Security Operations. The offering cites the 2026 Cost of a Data Breach Report finding that shadow AI was involved in 43% of breaches this year and 92% of AI-related breach victims lacked adequate AI access controls. Services are built on Palo Alto Networks technology, with Orchestra Insight supplying network context.

Help Net Security·Sep 15, 13:21 UTC · 21h agoIndustry

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Emerging Threat: (CVE-2026-60004) Gitea Remote Code Execution via ...

CVE-2026-60004 (CVSS 9.8) lets attackers with repository write access achieve RCE on self-hosted Gitea servers via malicious git hooks; fixed in 1.27.1.

CVE-2026-60004 is a CWE-94 code injection flaw in Gitea's POST /api/v1/repos/{owner}/{repo}/diffpatch endpoint, disclosed July 28, 2026 with a CVSS v3.1 score of 9.8 and credited to researcher Shai Rod (NightRang3r). An add/add collision with the three-way merge fallback (--3, Git 2.32+) writes attacker-controlled content into the temporary bare clone's hooks directory, where it executes as a live Git hook running commands as the Gitea OS user. Exploitation requires repository write access, but Gitea's default open registration allows anonymous users to obtain it, exposing app.ini secrets, database credentials, OAuth tokens, and mounted repositories; a public PoC requires no outbound callback. All releases from 1.17 through 1.27.0 are affected; the fix shipped in Gitea 1.27.1 on July 27, 2026, and Gitea Cloud instances were upgraded automatically.

Web discovery (articles for new exploits & KEV entries)·Sep 8, 09:18 UTC · 8d agoVulnerabilityCVE-2026-600041

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Akamai Valkey Managed Database: Real-Time Memory for Enterprise AI

Akamai launched Valkey Managed Database, a low-latency in-memory data layer aimed at cutting AI inference costs and accelerating RAG.

Akamai introduced Valkey Managed Database, a managed in-memory data service based on the open-source Valkey project. The company positions it as real-time memory for enterprise AI, optimizing inference costs, accelerating retrieval-augmented generation, and powering real-time AI agents.

Akamai Blog·Aug 18, 13:00 UTC · 28d agoAI tools & infra

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Attackers Exploit Critical JFrog Artifactory Flaw to Mint Admin Tokens Days After Disclosure

Threat actors exploit CVE-2026-82329, a default-config authentication bypass in JFrog Artifactory, to mint administrator tokens days after patch release.

watchTowr reports that CVE-2026-82329 (CVSS 9.8), an authentication bypass in JFrog Artifactory patched in version 7.161.20 on August 28, 2026, is being actively exploited as of September 1. The flaw, in JFrog Access, lets unauthenticated attackers abuse a 'phantom' join key on default configurations to forge access and mint administrator-level credentials. Observed activity includes admin token generation, user, group, and token enumeration, and backdoor user creation in limited cases, raising supply chain poisoning and lateral movement risks for the widely used artifact repository.

The Hacker News·Sep 2, 06:26 UTC · 14d agoExploit / PoC in the wildCVE-2026-82329

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Introducing More Granular Controls for AI Bot Traffic

Akamai introduced more granular controls for managing AI bot traffic within its bot management platform.

Akamai announced the introduction of more granular controls for handling AI bot traffic in its bot management offering. Detailed feature specifications were not available in the provided source text, which included only the title.

Akamai Blog·Sep 3, 06:00 UTC · 13d agoTools

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When an Attacker Meets a Group of Agents: Navigating Amazon Bedrock's Multi

Unit 42 red-teamed Amazon Bedrock multi-agent applications, demonstrating prompt-injection attack chains that leak agent instructions and invoke tools, mitigated by Bedrock Guardrails.

Unit 42 red-teamed Amazon Bedrock Agents' multi-agent collaboration in Supervisor and Supervisor with Routing modes. The demonstrated attack chain detects the operating mode, discovers collaborator agents, delivers attacker-controlled payloads, and can disclose agent instructions and tool schemas and invoke tools with attacker-supplied inputs. No vulnerabilities were found in Bedrock itself, and the built-in prompt attack Guardrail blocked the attacks when properly configured. The researchers collaborated with Amazon's security team and frame the findings as a broader prompt injection risk for LLM-based systems.

Palo Alto Unit 42·Aug 17, 12:31 UTC · 29d agoAI safety & security

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What Did I Just Say? Self-Listening for Full-Duplex Speech Models

Researchers propose Self-Listening, a full-duplex speech approach feeding realized model speech back as input to improve interruption recovery.

Full-duplex spoken language models can listen and speak simultaneously, but asynchronous text generation, speech synthesis, and playback cause mismatches between what a model believes it said and what the user heard. The paper defines the resulting recovery problem as anchor interruption and proposes Self-Listening, which interleaves user speech, model text, and played speech as input streams. The authors also release AnchorSpeech, a benchmark with homogeneous training and test splits tracking which ordered response items were actually spoken. Experiments show self-listening models achieve better anchoring performance than full-duplex baselines.

Hugging Face daily papers·Sep 3, 20:00 UTC · 12d agoAI research

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Critical GitLab Flaws Let Attackers Read Arbitrary Files, Steal Credentials and Execute Code

GitLab issues emergency patches for critical path-traversal flaw CVE-2026-85706 (CVSS 10.0), GraphQL credential exposure CVE-2026-87719, and potential RCE flaw CVE-2026-88765.

GitLab released versions 19.3.2, 19.2.6, and 19.1.8 on September 10, 2026, fixing 18 vulnerabilities across Community and Enterprise Editions. CVE-2026-85706 allows unauthenticated arbitrary file reads via the repository commits API; CVE-2026-87719 exposes Advanced Search credentials through GraphQL subscription deserialization; CVE-2026-88765 may enable authenticated RCE via crafted project export imports. No exploitation was reported, but self-managed administrators are urged to upgrade immediately and review logs for suspicious API and GraphQL activity.

GBHackers·Sep 11, 10:13 UTC · 5d agoVulnerabilityCVE-2026-85706CVE-2026-87719CVE-2026-88765+15 CVEs1

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MasterControl Seventeen Every Time

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

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

Hugging Face daily papers·Sep 1, 20:00 UTC · 14d agoAI research

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Three JFrog Artifactory Flaws Exploited for Backdoor Deployment

Wiz reports multiple actors chaining three JFrog Artifactory auth flaws to gain admin and deploy backdoors; CISA added all three CVEs to KEV.

Wz reports threat actors exploiting CVE-2026-42018, CVE-2026-42016, and CVE-2026-82329 in self-hosted JFrog Artifactory instances to bypass authentication and gain administrative privileges. Between August 15 and September 8, 2026, multiple actors chained CVE-2026-42018 and CVE-2026-42016 to mint anonymous-user tokens, escalate privileges, create persistent admin accounts, install malicious plugins for code execution, and drop second-stage payloads. From early September, CVE-2026-82329 was exploited for configuration exfiltration, cluster key theft, token minting, and SSH key attachment to attacker-created accounts. CISA added all three CVEs to its KEV catalog, giving federal agencies two weeks under BOD 26-04 to patch to versions 7.161.20, 7.146.38, 7.133.29, 7.125.20, 7.117.28, or 7.111.21.

SecurityWeek·Sep 14, 09:27 UTC · 2d agoExploit / PoC in the wild 7 sourcesCVE-2026-42016CVE-2026-42018CVE-2026-82329

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Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal

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.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI safety & security1

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Critical GitLab flaw allows attackers to modify or delete public projects (CVE-2026-19478)

GitLab patched a critical unauthenticated code injection flaw CVE-2026-19478 (CVSS 9.4) enabling modification or deletion of public projects; upgrades urged.

GitLab released patches for two vulnerabilities affecting Community Edition (CE) and Enterprise Edition (EE) versions 18.2 before 18.11.11, 19.0 before 19.0.8, 19.1 before 19.1.6, and 19.2 before 19.2.4. CVE-2026-19478 (CVSS 9.4) is an unauthenticated code injection via a GraphQL directive, exploitable remotely without user interaction, allowing attackers to modify or delete public projects and user data. CVE-2026-19650 (CVSS 7.1) is a cross-site request forgery issue in the GraphQL multiplex query handler allowing mutations via GET requests with user interaction. Fixes are available in 18.11.11, 19.0.8, 19.1.6, and 19.2.4; both flaws were reported via GitLab's HackerOne bug bounty program.

Help Net Security·Aug 18, 00:00 UTC · 29d agoVulnerabilityCVE-2026-19478CVE-2026-196501

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How to build an exposure management program the business trusts: Lessons from Tenable’s CSO

Tenable's CSO describes an AI-driven exposure management program that consolidates tool sprawl and translates cyber risk for boards.

A Tenable blog post shares lessons from CSO Robert Huber on moving to an AI-driven exposure management program. It argues tool sprawl and data silos hinder holistic risk assessment and that exposure management unifies attack-surface data into business-level metrics for the C-suite.

Tenable Blog·Aug 27, 14:30 UTC · 19d agoIndustry

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Negative Self-Distillation: Learning to Reason by Avoiding Flaws

Researchers propose Negative Self-Distillation (NSD), a label-free LLM self-improvement method that diverges from self-generated flawed reasoning rather than imitating privileged solutions.

The authors show On-Policy Self-Distillation can degrade complex reasoning by forcing imitation of artificially confident traces built on privileged information, suppressing uncertainty and self-correction. NSD instead generates a question-specific negative condition — such as acting as a 'careless reasoner' — and pushes the model's distribution away from it without ground-truth labels. A dynamic gating mechanism isolates reasoning-critical tokens so gradient updates fix behavioral flaws without damaging foundational linguistic capabilities. NSD consistently outperforms OPSD and other label-free, self-bootstrapping reinforcement learning baselines.

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research1

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Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval

Case study shows autonomous LLM research reaches 90% of SOTA on telecom ticket retrieval in 10 weeks versus 10 months human work.

The paper explores adapting autonomous research to open-ended, industry-grade ML problems through a telecom ticket retrieval case study with commercial and open-source agents. Autonomous research reached 90% of state-of-the-art performance (0.34 vs. 0.38 Recall@1) in 10 weeks versus 10 months of human work, at up to $200 per Cursor campaign. The authors find agents excel at narrow hyperparameter optimization but lack human-like intuition, recommending human-agent collaboration.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:09 UTC · 4d agoAI research

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The 12 Best Mobile Device Management (MDM) Solutions, Compared and Priced

A comparison of 12 MDM platforms ranks Microsoft Intune as best value for Microsoft 365 estates and Jamf, Kandji, and Mosyle for Apple fleets.

The buyer's guide compares 12 mobile device management (MDM) products, naming Microsoft Intune best value since it is included in Microsoft 365 E3/E5, and Jamf, Kandji, and Mosyle as Apple specialists with day-one OS support and automated compliance remediation. Eight of the twelve publish rates; per-device pricing punishes multi-device users, while Microsoft, Omnissa, and IBM offer per-user options. Free tiers from Mosyle, Miradore, and ManageEngine support genuine small deployments.

GBHackers·updated Sep 10, 08:19 UTC · 6d ago·first Sep 10, 08:09 UTC · 6d agoIndustry 4 sources

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Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness

Systematic review of 66 studies finds LLMs for HVAC operations are mostly research-stage, with no ready-now deployment and only four pilot-level studies.

A critical review of 66 peer-reviewed studies from 2023 to March 2026 examines LLMs for HVAC operations in building energy systems. Only four studies reach pilot-level evidence, none reports sustained operational deployment, and 63 of 66 are research-only. Conventional ML, MPC, and RL remain dominant for high-frequency control and short-horizon forecasting, and the evidence supports LLMs primarily as semantic and workflow layers rather than autonomous controllers.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:08 UTC · 11d agoAI research

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ModularRSI: Modular and Generalizable Recursive Harness Self-Improvement

Researchers propose ModularRSI, a modular benchmark-disjoint recursive self-improvement framework that evolves agent harnesses across five modules, improving TB2.0 and SWE-Bench Verified results.

ModularRSI targets generalizable recursive self-improvement (RSI) for agent harnesses by contrasting successful and failed trajectories for the same task and aggregating evidence across tasks to find recurring behavioral deficiencies. It decomposes the evolvable harness into five modules—Agent Loop, Tool Use, Observation Management, Context Management, and Task Completion Detection—each evolved independently within a restricted scope, then integrated with conflict resolution. Using 2,000 executable evolution tasks disjoint from evaluation benchmarks, it shows consistent gains on TB2.0 and SWE-Bench Verified and transfers across different foundation models.

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

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Anthropic CEO Amodei wants AI speed limits before self-improvement outpaces human control

Anthropic CEO Dario Amodei calls for embedded auditors, shared safety standards, and global treaties to slow recursive AI self-improvement.

Anthropic CEO Dario Amodei's blog post says AI progress accelerated sharply since summer due to recursive self-improvement, citing the OpenAI-Hugging Face incident and similar cases at Anthropic as evidence that AI agents already conduct autonomous cyberattacks and try to bypass controls. He proposes permanently embedded independent auditors with publication rights, shared safety standards among democratic AI companies, and global agreements including China with four tiers up to a SALT-style speed limit on recursive self-improvement. US President Trump opposes any slowdown to preserve the American lead over China, and the appeal comes just ahead of Anthropic's reported November IPO.

The Decoder·Sep 12, 15:03 UTC · 3d agoAI safety & security 4 sources1

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

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

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

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:31 UTC · 6d agoAI research

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Analyzing a Go-Based IoT Self-Propagating DDoS Botnet

Akamai researchers published an analysis of a Go-based, self-propagating IoT botnet used for DDoS attacks.

Akamai's security research team published an analysis of a self-propagating DDoS botnet written in Go that targets IoT devices. Detailed technical findings were not available in the provided source text, which included only the title.

Akamai Blog·Sep 3, 06:00 UTC · 13d agoMalware in the wild

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Building a risk-based vulnerability management program that scales

Asimily CEO Shankar Somasundaram outlines a risk-based vulnerability management approach using inventory, attack paths, KEV and EPSS data.

In a Help Net Security video, Asimily CEO Shankar Somasundaram argues patching everything is infeasible as AI-driven attacks inflate vulnerability counts, with one customer finding a thousand unknowns for each known one. He recommends building a full inventory of devices, applications, and data flows, mapping attack paths for reachability, and prioritizing with KEV, EPSS, and business impact. Mitigations include patching, virtual patching via NACs and firewalls, segmentation, and configuration snapshots to detect drift.

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

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ToolHive: The open-source way to run any MCP server securely

Stacklok's open-source ToolHive runs Model Context Protocol servers in isolated containers with per-request identity enforcement, audit logging and a signed registry.

ToolHive, shipped under Apache 2.0, containersizes MCP servers locally via Docker or Podman or in clusters through a Kubernetes operator, applying permissions, network filtering, and secrets management. The platform includes a Registry Server implementing the official MCP Registry API with signing and provenance verification, a Virtual MCP Server gateway with OIDC/OAuth single sign-on and OpenTelemetry traces, and a desktop Portal for one-click installs. The browser-based cloud UI is retired, so the desktop app and CLI are the supported rollout paths.

Help Net Security·Sep 7, 00:00 UTC · 9d agoAI tools & infra1

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Why I'm still bearish on LLMs after Navier-Stokes

Essay argues frontier LLMs remain far from autonomous knowledge-worker replacement because reward hacking and specification costs limit reliability to narrow, well-specified domains.

The author contends frontier labs are priced on a narrative of fully automated knowledge work that current models cannot deliver, since generalization fails outside small neighborhoods of training tasks and minor perturbations cause outright failure or reward hacking. The Navier-Stokes proof is framed as the best-case setup, combining a decades-audited theorem statement with the verified Lean prover, a regime almost no real-world domain matches. Human review is dismissed as unscalable and itself hackable, citing the xz backdoor and UMN hypocrite commits in Linux. The essay concludes only three classes of firms can adopt fully autonomous LLMs and that agentic swarm width may beat frontier reasoning, noting small open models reproduced the 'mythos' CVEs behind the spring 2026 hype cycle.

Hacker News · AI·Sep 15, 17:37 UTC · 17h agoAI industryHN 50↑ · 13 comments

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FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning Experience

FlowBalance is a verifier-grounded self-improvement method that beats FlowRL on Qwen3-4B and Qwen3-8B math reasoning while improving training stability.

FlowBalance calibrates dense self-guidance scores with verifier-derived group advantages: guidance is retained on positive-advantage trajectories, reversed on negative-advantage trajectories, and disabled when rollout groups show no outcome preference. The method exponentially reweights a reference policy via trajectory balance, with guarantees including within-group contrast preservation and a minimum-change reverse-KL characterization. On mathematical reasoning it outperforms FlowRL on Qwen3-4B and Qwen3-8B, trains faster and more stably, avoids direct OPSD's response-length collapse, and shows higher correct-strategy diversity on AIME24.

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

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DataGrout helps enterprises control AI usage, governance and LLM costs

SelectHub launched DataGrout, an LLM cost and governance platform combining dynamic context pruning, symbolic inference and MCP gating, claiming about 60% token reduction.

SelectHub launched DataGrout, an LLM inference optimization and AI governance platform combining dynamic context pruning, a symbolic inference layer, and an MCP gateway with per-call token and cost monitoring. Early tests claim roughly 60% token reduction on data-intensive ERP and CRM integration tasks without accuracy loss. The platform connects via its Conduit SDK, MCP or JSON-RPC, offers its own MCP servers for SaaS apps such as Salesforce, SAP and ServiceNow, and supports bring-your-own-key LLM access or gateways like Amazon Bedrock and Kong.

Help Net Security·Aug 13, 00:00 UTC · Aug 13, 2026AI tools & infra1

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