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Parallels Desktop Vulnerability Lets Non-Admin Mac Users Execute Code as Root

JFrog researchers disclosed CVE-2026-90894, a critical Parallels Desktop local privilege escalation letting non-admin Mac users execute code as root; fixed in 27.0.0.

JFrog researchers found that Parallels Desktop's privileged prl_disp_service daemon on macOS accepts unsigned local clients through a world-writable socket and allows argument injection into a tar command during appliance installation. Injecting the --use-compress-program option makes tar execute an attacker-controlled program as root, and a one-line proof of concept yielded a root shell without needing a running virtual machine. The flaw, dubbed ParaShells, was confirmed in Parallels Desktop 26.4.0 build 57513 on Apple Silicon and is fixed in version 27.0.0. No exploitation in the wild has been reported; administrators should inventory and upgrade affected installations and restrict local account access.

Cyber Security Newsupdated · 21h agofirst · 1d agoVulnerability 4 sourcesCVE-2026-90894

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

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

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

Hacker News · AI · 10d agoAI tools & infraHN 89↑ · 53 comments2

Local Privilege Escalation (LPE) in FolkPatch due to Hardcoded Default SuperKey

A hardcoded default SuperKey in FolkPatch, an APatch-based kernel patching tool, enables local privilege escalation limited to FolkPatch's downstream code.

A disclosure posted to the oss-security mailing list describes a local privilege escalation (LPE) in FolkPatch caused by a hardcoded default SuperKey. FolkPatch is a downstream project based on APatch that utilizes its own custom KernelPatch. According to the post, the vulnerability is specific to FolkPatch's downstream modifications rather than upstream APatch or KernelPatch code.

oss-security · 4d agoVulnerability1

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

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

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

Latent Space · 21d agoAI industry

Any user process can escalate to root

A disclosed local privilege escalation flaw allows any user process to escalate to root on the affected system.

A Lobsters-linked security write-up describes a vulnerability in which any user process can escalate its privileges to root. The feed text provides no product name, CVE identifier, CVSS score, or exploitation details, limiting available detail. Local privilege escalation flaws are commonly chained with other issues for full system compromise, so defenders should review the full write-up for affected versions and patches.

Lobsters · security · 16d agoVulnerability

How much of F-Droid is LLM generated?

A FOSS maintainer manually graded 102 F-Droid apps from the September 12, 2026 update batch, finding many show signs of LLM-generated code.

A student and FOSS app maintainer reviewed 102 apps pushed to F-Droid on September 12, 2026, assigning each a three-tier rating for likelihood of LLM-authored code (mostly AI >50%, hard to say/mostly human, no signs of AI). The heuristic relies on commit aesthetics, README and branding style, and the presence of agentic infrastructure like Claude Code or Codex, which automatically places an app in the 'mostly AI' tier. Example ratings include Amber (Nostr event signer) as mostly AI, and Aria for Misskey as showing no AI signs. The author stresses reliable detection of LLM-generated code from text alone is impossible, so ratings are approximate.

Hacker News · AI · 2d agoAI researchHN 46↑ · 23 comments

Inoculation Midtraining with Learned Neologisms

Inoculation Midtraining confines unsafe LLM behavior to a neologism-marked context, reducing misalignment after unsafe post-training but leaking under nearby contextual cues.

The paper introduces Inoculation Midtraining, which teaches a base model during midtraining that unsafe behavior belongs to a context marked by a learned neologism token, then post-trains on unsafe data within that context. Across supervised fine-tuning and RL post-training regimes, the technique reduces misalignment while preserving transfer of benign properties like German or Shakespearean prose. However, it does not outperform standard Inoculation Prompting, is sensitive to training configuration, and produces a leaky boundary that nearby contextual cues can reactivate. The authors conclude it is not yet a load-bearing component of a developer safety framework.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI safety & security

LLMs are real, AI is fake

Cory Doctorow argues the OpenAI chatbot 'hacking' of Hugging Face was a Python-scripted CTF loop, not autonomous AI.

In an opinion essay, Cory Doctorow debunks reports that OpenAI chatbots autonomously hacked Hugging Face servers during an 'Exploit Gym' capture-the-flag challenge. He explains the chatbot merely acts as a front-end queried by a Python program that replays commands drawn from CTF training data. He argues sensational 'AI went rogue' narratives are amplified by technical press and help AI companies raise investment capital.

Hacker News · AI · 4d agoAI safety & securityHN 40↑ · 10 comments1

Linux Detection Engineering - Local Privilege Escalation

Elastic details a layered detection framework for Linux local privilege escalation, covering 2026's copy-on-write bug wave and LLM-assisted discovery.

Elastic Security Labs describes how most Linux local privilege escalations share a common host flow — an unprivileged process launched from a writable path becoming root — and proposes layered detections combining general outcome-based rules with per-technique rules in Elastic Defend and Auditd. It tracks 13 recent LPE disclosures, seven of which share a copy-on-write/zero-copy bug class, including Copy Fail, DirtyFrag, Fragnesia, DirtyDecrypt, DirtyClone, pedit COW, and RefluXFS. Qualys attributes RefluXFS to an LLM-assisted research effort with Anthropic using Claude Mythos Preview, and another bug is credited to an LLM-assisted workflow. Detection and endpoint rules are published in Elastic's detection-rules and protections-artifacts repositories.

Elastic Security Labs · 6d agoResearch1

[webapps] Ray 2.56.0 - Directory Traversal & Local File Inclusion

Public exploit released for directory traversal and local file inclusion in Ray 2.56.0, the widely used distributed ML framework.

Exploit-DB entry 52635 publishes a web application exploit targeting Ray 2.56.0, the distributed computing framework from Anyscale commonly used for ML workloads. The PoC demonstrates a directory traversal and local file inclusion condition, allowing attackers to read files outside the intended path on an exposed Ray instance. Ray dashboard instances exposed to the internet could leak sensitive files such as credentials and configuration.

Exploit-DB · Aug 11, 2026Exploit / PoC

ZDI-26-569: Linux Kernel Net Scheduler True Link Equalizer Race Condition Local Privilege Escalation Vulnerability

ZDI publishes ZDI-26-569, a CVSS 7.5 race condition local privilege escalation in the Linux kernel net scheduler true link equalizer.

The Zero Day Initiative disclosed a race condition in the Linux kernel's net scheduler true link equalizer component enabling local privilege escalation. Exploitation requires the attacker to first run high-privileged code on the target system. The advisory carries a CVSS rating of 7.5; no CVE id is listed in the disclosure text.

ZDI Published Advisories · Aug 13, 2026Advisory

Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM

French BabyLM entry METRON-FR (125M GPT-2, 92.47M words) shows tokenizer artifacts dominate child-scale zero-shot evaluation; proposes standard diagnostics.

METRON-FR is a 125M-parameter GPT-2 pretrained on 92.47M French words, submitted to the BabyLM 2026 Strict track, scoring 85.97% on the native Quebec-French QFrBLiMP benchmark and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE protocol combining French task-data translation with rank-16 LoRA shows relational tasks gain while world-knowledge tasks regress. Bilingual Lexicon Induction reaches p@1 of 68.84%, 18x above chance, and ablations show single-token zero-shot scoring is dominated by tokenizer and template artifacts at child scale.

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

Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model

Study shows visually grounded token embeddings in a small masked LM persist through training and improve object-property knowledge, but escape standard BabyLM benchmarks.

The paper implements ostensive definition for a small DeBERTa masked language model trained on 10M words, seeding visually grounded tokens with embeddings derived from labeled image regions before training. Visual initialization leaves a persistent, seed-replicated advantage on object-property knowledge (COMPS) and a corpus-tailored Visual-Property Swap benchmark covering color, material, size, and shape, but has no effect on most BabyLM grammar benchmarks. Synthetic grounding of previously unseeded words causally transfers the advantage to exactly those words.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research

[0day-rubbish] Royal Server 5.04.50529.0 Local privilege escalation to LocalSystem on the execution path without credential override (7.2)

0day Rubbish Research Team disclosed a CVSS 7.2 local privilege escalation (CWE-250) in Royal Server 5.04.50529.0 to LocalSystem.

Researchers publicly disclosed a local privilege escalation vulnerability in Royal Server 5.04.50529.0, classified as CWE-250 (execution with unnecessary privileges) with a CVSS score of 7.2. The flaw allows escalation to LocalSystem on the execution path without credential override. The disclosure was posted to the Full Disclosure mailing list on September 8, 2026; no patch or CVE id was mentioned in the notice.

Full Disclosure · 8d agoVulnerability

MetroLLM-Bench: Evaluating Language Models as Transit Kiosk Runtimes

MetroLLM-Bench is a 955-case benchmark testing language models as transit kiosk tool-calling runtimes across six real metro systems.

The benchmark covers 37-414-station metro systems and eleven task categories including routing, fare calculation, disruptions, accessibility, and adversarial input, with 14 deterministic and 8 semantic scoring components. Of 26 models from six vendors, a PEFT-tuned 4B Qwen 3.5 student scored 91.3 on Tier 1, exceeding GPT-5.6 (90.6/90.0), while Muse Glimmer 30B led the composite ranking. A deterministic rule-based baseline reached 84.6, and PEFT gains over base models shrank from +7.03 points at 2B to -0.91 at 27B.

Hugging Face daily papers · 8d agoAI research1

The Illusion of Local Privacy: Confidentiality Boundary Failures in Consumer LLM Serving Systems

Researchers show local LLM serving systems leak prompts via memory residue, plaintext persistence, a llama.cpp tenant-isolation flaw, and timing oracles.

A study of consumer local-LLM serving systems identifies four boundaries where prompt confidentiality fails: model loading, runtime memory, wrapper persistence, and the serving interface. Using the LLAnalyzer framework across four open-weight model families and two deployment platforms, the authors recover plaintext prompts from allocator-managed memory after inference and show wrappers extend prompt lifetime. They also uncover a previously undocumented llama.cpp authorization flaw letting one authenticated client restore another tenant's saved conversation state, succeeding in 200/200 trials, plus a remote timing oracle via shared prompt-prefix caching that works over WAN.

arXiv cs.CR · 22h agoAI safety & security

Convergent Emergence of In-Context Learning Across Modalities

Controlled experiments show few-shot in-context learning emerges across six modalities including language, genomes, images, and proteins, partially supporting a convergence hypothesis.

The paper tests the Convergent Emergence Hypothesis: that few-shot in-context learning, when it emerges, shares a common cross-modality difficulty profile. A controlled framework instantiated the same task suite across six modalities: language, genome, integer sequences, time series, images, and proteins. Paired-mapping ICL emerged in all six modalities, surpassed controlled baselines, and showed correlated per-task effects in five of them, providing partial support for the hypothesis.

Hugging Face daily papers · 5d agoAI research

ZDI-26-608: Linux Kernel KVM IOAPIC Use-After-Free Local Privilege Escalation Vulnerability

The Linux kernel KVM IOAPIC has a use-after-free (CVSS 8.2) allowing local privilege escalation, but exploitation requires high-privileged code execution first.

ZDI-26-608 describes a use-after-free vulnerability in the Linux kernel's KVM IOAPIC component, with a CVSS score of 8.2. An attacker must first obtain the ability to execute high-privileged code on the target system, which limits the practical impact of the privilege escalation. The advisory text does not list an assigned CVE identifier.

ZDI Published Advisories · 24d agoVulnerability

ActGuard: Pre-execution Action Auditing against Indirect Prompt Injection in LLM Agents

ActGuard audits LLM agent actions before execution against predicted tool priors, masking only malicious spans from indirect prompt injections while preserving utility.

ActGuard is a pre-execution action auditing framework against indirect prompt injection in LLM agents, judging whether external content causes the current action to deviate from a locally reasonable expectation rather than whether content is inherently suspicious. At each step it predicts the tools likely used by the upcoming action, builds a local tool prior, then performs tool-level contrastive analysis and parameter-level evidence localization to identify deviations. A verifier masks only spans confirmed as malicious and regenerates the action from the sanitized context. On challenging tool-using agent benchmarks it reduces attack success to state-of-the-art levels while keeping task utility close to the no-attack setting; code is publicly available on GitHub.

arXiv cs.CR · 3d agoAI safety & security

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 · 14d agoAI safety & security1

CVE-2026-82430: Apache Storm Worker Launcher: Local Privilege Escalation to Root via Container Command Files Chowned to the Tenant

Setuid-root Apache Storm worker-launcher chowns worker directory to the tenant before reading its command file, enabling local root privilege escalation.

CVE-2026-82430 affects Apache Storm Worker Launcher (org.apache.storm:storm-core) versions 3.0.0 before 3.1.0, rated important. When launching Docker or OCI workers, the setuid-root worker-launcher transfers ownership of the entire worker directory to the untrusted topology user before reading and acting on the supervisor-written command file in that directory. An untrusted tenant can tamper with the command file to achieve local privilege escalation to root. Fixed in version 3.1.0.

oss-security · 3d agoVulnerabilityCVE-2026-82430

Omarchy: Any User Process Can Escalate to Root

A security write-up details how any user process on the Omarchy Linux setup can escalate privileges to root.

A post on 0xcc.io describes a local privilege escalation issue in Omarchy, the Arch-based Linux configuration, where any user process can escalate to root. The disclosure appears to describe a design-level weakness in the distribution's security model. No CVE identifier is mentioned in the available text.

Lobsters · security · 17d agoVulnerability

Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

An open methodology toolkit measures whether transformer language models contextualize fixed word forms across domains using bridge forms and layer-wise silhouette analysis.

The manual documents an open toolkit built around 'bridge forms' - identical written words recurring across two or more subject domains with a different sense in each - to test whether transformer language models individuate word occurrences by context beyond the embedding layer. It covers declarative specification of bridge forms, Wikipedia corpus acquisition, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and visualization, justifying each choice against failure modes such as sense contamination and subword-tokenization misalignment. It is a methodological and implementation reference and reports no empirical results.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research

[webapps] Langflow 1.8.4 - Path Traversal to Remote Code Execution

A path traversal to remote code execution exploit for Langflow 1.8.4, a popular LLM application builder, was published on Exploit-DB.

Exploit-DB lists a proof-of-concept exploit chaining path traversal to remote code execution in Langflow 1.8.4, an open-source tool used to build LLM applications and agents. The chain allows an attacker to write arbitrary files outside the intended directory and achieve code execution on the host. The provided text does not include a CVE identifier or reports of exploitation in the wild, but RCE in a widely deployed AI tooling product is notable for defenders.

Exploit-DB · 17d agoExploit / PoC1

The Router Within: Eliciting Native Skill Routing from a Frozen LLM

Gavel reads native skill-routing signals from a frozen LLM's forward passes with two linear maps, beating retrieve-and-rerank pipelines by up to 21.9 points on Qwen3-32B.

Gavel (Glance And Verdict from a frozen LLM) elicits skill routing from a frozen agent LLM using two trained linear maps that read mid-layer states, keeping all skill text out of context. A glance step scores the full library against compact per-skill banks built in one forward pass at installation; a verdict step resumes shortlisted skills' forward passes and fuses likelihood and yes/no judgments as a product of experts. It transfers zero-shot to three public benchmarks plus SkillTraj, a new benchmark of 372 simulated agent trajectories. On Qwen3-32B it beats progressive disclosure and retrieve-and-rerank pipelines adding 1.2B–16B external parameters by up to 13.4 points on written tasks and 21.9 when skills are needed mid-rollout.

Hugging Face daily papersupdated · 2d agofirst · 3d agoAI research 2 sources

ZDI-26-574: Linux Kernel Net Scheduler Connection Tracking Race Condition Local Privilege Escalation Vulnerability

A race condition (CVE-2026-46319, CVSS 7.5) in the Linux kernel net scheduler connection tracking allows local privilege escalation.

ZDI advisory ZDI-26-574 documents a race condition in the Linux kernel's net scheduler connection tracking component. Local attackers who can execute high-privileged code on a target can exploit the flaw to escalate privileges. ZDI rated the vulnerability 7.5 on CVSS and assigned CVE-2026-46319.

ZDI Published Advisories · Aug 13, 2026VulnerabilityCVE-2026-46319

ZDI-26-568: Linux Kernel Net Scheduler Race Condition Local Privilege Escalation Vulnerability

ZDI disclosed a race condition (CVSS 7.5) in the Linux kernel net scheduler enabling local privilege escalation; no CVE assigned in the advisory text.

ZDI advisory ZDI-26-568 describes a race condition in the Linux kernel's net scheduler that allows local attackers to escalate privileges on affected installations. Exploitation requires the attacker to first execute high-privileged code on the target system. ZDI assigned a CVSS rating of 7.5; no CVE identifier is listed in the advisory text.

ZDI Published Advisories · Aug 13, 2026Vulnerability

Encoded Early, Used Late: Where Transformers Begin to Act on an Inferred Partner's Expertise

Probing finds transformers represent an inferred dialogue partner's expertise in early layers long before it causally influences output.

Using ExpertCollab, a corpus of multi-turn research-planning dialogues between model-played personas at four expertise levels, researchers show that a partner's inferred expertise is most decodable in early transformer layers and decays to near chance before the network's midpoint. Counterfactual patching reveals that injecting the expertise difference at peak decodability barely changes a fixed late-layer readout, while injection past the midpoint propagates almost completely. The result bounds where readout or steering of partner-conditioned behavior must intervene, demonstrated on a single model with a synthetic corpus.

Hugging Face daily papers · 10d agoAI research

Flextype v1.0.0-alpha.3 CMS registerShortcodes() Remote Code Execution via Attacker-Controlled File Inclusion

Flextype CMS v1.0.0-alpha.3 allows PHP remote code execution via path traversal in the Entries API combined with shortcode file inclusion.

Flextype CMS v1.0.0-alpha.3 exposes a remote code execution path through the interaction of the Entries API and Shortcodes::registerShortcodes(). The /api/v1/entries endpoint accepts attacker-controlled entry identifiers containing path traversal sequences, allowing PHP-containing content to be written outside the intended entries directory. A subsequent attacker-controlled path can then be included and executed as PHP. Ron E posted the disclosure to the Full Disclosure mailing list on September 3, 2026.

Full Disclosure · 13d agoVulnerability 8 sources

ZDI-26-606: Microsoft Windows Compatibility Appraiser Link Following Local Privilege Escalation Vulnerability

ZDI disclosed a link-following flaw in Windows Compatibility Appraiser (CVSS 7.0) enabling local privilege escalation from the LOCAL SERVICE context.

ZDI-26-606 describes a link-following vulnerability in the Windows Compatibility Appraiser component that permits local privilege escalation. An attacker must already be able to execute low-privileged code in the LOCAL SERVICE context on the target system. ZDI rated the issue 7.0 on the CVSS scale; the advisory lists no CVE identifier. No active exploitation is reported.

ZDI Published Advisories · 24d agoVulnerability2

ZDI-26-575: Linux Kernel Net Scheduler Packet Classifier API Time-Of-Check Time-Of-Use Local Privilege Escalation Vulnerability

ZDI publishes ZDI-26-575, a CVSS 7.5 TOCTOU local privilege escalation in the Linux kernel net scheduler packet classifier API.

The Zero Day Initiative disclosed a time-of-check time-of-use flaw in the Linux kernel's net scheduler packet classifier API that permits local privilege escalation. Exploitation requires the attacker to first execute high-privileged code on the target system. ZDI assigned a CVSS rating of 7.5; no CVE id is provided in the text.

ZDI Published Advisories · Aug 13, 2026Advisory1

NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

An 8.9B-parameter latent-space language model using next-concept prediction matches OLMo-3-7B pretraining loss with only 51.3% of the training tokens.

NCP-ArchPreview augments next-token prediction with Next Concept Prediction over a product-quantized concept vocabulary built from hidden states, trained jointly end-to-end. The 8.9B model was trained on 5.73T tokens from the Dolma-3 dataset, the largest latent-space language model demonstration to date. It consumes 51.3% of the tokens to reach OLMo-3-7B's final pretraining loss and outperforms it by 2.45 points on the downstream macro-average, including a 5.99-point GSM8K gain. The learned latent space also enables lightweight domain adaptation via a 17M-parameter VQ module and improves speculative drafting accepted length by 4.17%.

Hugging Face daily papers · 8d agoAI research1

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 · 1d agoAI research