Search: “KPMG”

Re: Retrospective by 'gpg.fail' authors

oss-security follow-up to a gpg.fail authors' retrospective argues that GnuPG's unmaintained, forgotten code is a security liability and should be deleted.

On the oss-security mailing list, Soatok Dreamseeker responds to a retrospective published by the gpg.fail authors, asking why unmaintained code is retained and stating that forgotten, unmaintained code is a liability and effectively unmaintained by definition. The visible reply addresses Werner Koch and concerns GnuPG code maintenance. No specific CVE, flaw, or exploitation is described in this snippet.

oss-security·17:16 UTC · 6h agoVulnerability 8 sources1

Cybersecurity jobs available right now: February 10, 2026

Help Net Security's roundup lists open cybersecurity roles at KPMG, Pentera, Google, Group-IB and others across multiple countries.

A job-board roundup featuring Cloud Security Engineer at KPMG (Israel), Cloud Security Researcher at Pentera (Israel), Cyber Defence Senior Analyst at Google (UK), and Cyber Investigation Specialist at Group-IB (UAE). Additional listings cover SOC operations, penetration testing, network architecture, OT/IT convergence and AI/ML security testing across Australia, Italy, the US, India, France, Ireland and the UAE. All listings are marked no longer accepting applications.

Help Net Security·Aug 26, 09:46 UTC · 21d agoIndustry

New Guidance Helps Businesses Verify Quantum-Safe Hardware Claims

TCG issued guidance to help businesses verify that trusted platform modules genuinely meet quantum-safe requirements.

The Trusted Computing Group (TCG) published new guidance aimed at proving that trusted platform modules (TPMs) genuinely satisfy essential quantum-safe requirements. The document gives businesses a way to validate vendor claims about post-quantum readiness in hardware security rather than trusting marketing assertions. This is a guidance publication, not an incident or vulnerability disclosure.

Infosecurity Magazine·Aug 24, 16:00 UTC · 23d agoAdvisory

OpenAI says Daybreak will expand to offer specialized cyber services

CyberScoop·Aug 10, 20:55 UTC · Aug 10, 2026Exploit / PoC

New TCG guidance gives buyers a way to test PQC-ready TPM claims

Trusted Computing Group published PTP 1.07-based requirements defining PQC-ready and PQC-upgradable TPM designations to guide buyers assessing quantum-safe hardware.

The Trusted Computing Group published requirements establishing PTP 1.07, based on TPM 2.0 Library Specification Version 1.85, as the baseline for a 'TCG PQC-ready TPM', and defined a second designation, 'TCG PQC-upgradable TPM', for chips that can be upgraded to that support. The guidance lets buyers request vendor evidence that TPMs meet end-to-end post-quantum requirements for identities, attestation, and hardware-anchored trust rather than single-algorithm compliance. TCG also announced plans to extend its certification programs to certify TPMs meeting PTP 1.07, noting that 90% of businesses still lack a formal PQC roadmap.

Help Net Security·Aug 25, 00:00 UTC · 22d agoTools

Your security vendor gets the frontier cyber model, you get the findings

Help Net Security·Aug 11, 00:00 UTC · Aug 11, 2026Vulnerability

K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

Clinician-calibrated K-Bench evaluates 125 LLM configurations on 200 high-risk mental health vignettes, exposing wide variation in suicide and violence risk handling.

K-Bench is a clinician-calibrated, protected benchmark evaluating 125 model configurations from 33 base models across 14 providers on 200 multi-turn vignettes covering suicide, self-harm, domestic violence, substance misuse and no-risk presentations. A frozen GPT-4o judge achieved 94.2% exact agreement with clinician consensus across 6,751 eligible comparisons from 151 clinician-rated transcripts. Leading models combined supportive conversation with combined-risk scores above 95, while risk exploration varied substantially among weaker configurations; therapeutic prompting helped weaker models and elevated reasoning produced no average improvement. A continuously updated public leaderboard is hosted at k-bench.ai with protected test materials.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 16:49 UTC · 2d agoAI safety & security

E4del and PINHOLE RATs Turn FTP Banners Into Dead Drops for Malware Commands

Researchers identified two new RATs, E4del and PINHOLE, using FTP banners as dead drop resolvers to fetch C2 commands via ClickFix-style lures.

SOCRADAR documented two previously unreported RATs, E4del and PINHOLE, that use FTP banners as dead drop resolvers, a first in-the-wild use of this technique. E4del is a Node.js-based RAT hidden in a digitally signed Electron application masquerading as Discord, with tiered-jitter beaconing, reverse shell, screenshot capture and desktop streaming. PINHOLE is more advanced, using Pinterest, SurveyMonkey and Cloudflare Workers for C2 resolution, six layers of unpacking, Halo's Gate EDR evasion and Early Bird APC injection. A related WebDAV delivery chain overlaps with ClearFake campaigns distributing WordlistLoader and Amatera Stealer.

The Hacker News·Aug 25, 11:33 UTC · 22d agoMalware in the wild

Emergency Department Revisit Quality Review Screening: Exploring Human Decision-Making and Artificial Intelligence Support

A retrospective study found GPT-4 over-flagged emergency department revisit cases while an LLM knowledge-graph screener achieved 83-100% positive predictive value.

In an exploratory retrospective study of 99 emergency department diagnosis pairs from a multihospital health system, clinicians and GPT-4 independently judged whether revisit pairs warranted further assessment. GPT-4 responses correlated poorly with clinicians, flagging 94% of pairs for follow-up, 4.4-13.3 times more than clinicians, though prompt engineering was minimal. An algorithm leveraging an LLM-populated knowledge graph (KGA) achieved 83-100% positive predictive value against at least one clinician rater, suggesting LLM-based screening could broaden revisit quality review without substantially increasing reviewer workload.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:37 UTC · 7d agoAI research1

Anthropic's Enterprise Frontier Safeguards lets your Claude logs stay in your cloud

Anthropic launches Enterprise Frontier Safeguards, letting regulated customers keep Claude usage logs in their own cloud storage under their own keys with automated misuse detection.

Anthropic unveiled Enterprise Frontier Safeguards, co-designed with eight members of the Analysis and Resilience Center for Systemic Risk and more than 100 customers including Wells Fargo, Goldman Sachs, Visa, Mastercard, Comcast, KPMG, and Salesforce. Misuse-detection activity data can live in the customer's Amazon S3, Azure Blob Storage, or Google Cloud Storage under customer-managed keys, with fully automated review and no human inspection by Anthropic; automated systems watch for offensive cyber or biological capability development and signs of stolen credentials. The offering replaces the friction around Fable 5's 30-day retention policy, costs nothing extra, spans Claude Enterprise, Claude Platform, Amazon Bedrock, Google's Agent Platform, and Microsoft Foundry, with broad availability targeted for later this fall.

Help Net Security·Sep 2, 00:00 UTC · 14d agoAI industry1

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark of 660 specification-gap instances shows LLMs localize implementation-critical research gaps poorly, with best model at 9.6% defect recovery.

IdeaAMBIG is a benchmark of 660 evidence-grounded instances evaluating whether research-method specifications provide enough information for faithful implementation: 163 real-world gaps from reproducibility reports and GitHub issues plus 497 controlled synthetic gaps. It tests codification-readiness assessment, defect localization, and clarification action generation across 13 LLMs. The best model achieves only a 9.6% Macro Defect Recovery Rate on real-world instances, though 80.6% clarification success when given the annotated defect, and an oracle study shows gold resolutions raise codification-ready rates from 14% to 98%. Defect localization emerges as the main bottleneck across all evaluated models.

Hugging Face daily papers·Sep 8, 20:00 UTC · 8d agoAI research

Cybersecurity jobs available right now: December 16, 2025

Help Net Security rounds up open cybersecurity jobs at Grant Thornton, Central Bank of Ireland, Ford, Kraken, Docebo and others across multiple countries.

This is a job listing roundup covering cybersecurity openings at organizations including Grant Thornton, the Central Bank of Ireland, Ford Motor Company, Global Medical Response, banglalink, Mindrift, Kraken, PFH Technology Group, Kiwibank, Mazrui International, Docebo and Alpitronic. Roles span SOC operations, GRC, endpoint security, FedRAMP compliance, threat intelligence and privacy leadership across the USA, Ireland, India, Bangladesh, France, UAE, Canada and other locations. All listings were marked as no longer accepting applications at publication time.

Help Net Security·Aug 20, 10:46 UTC · 27d agoIndustry

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark with 660 instances measures whether LLMs can spot and fix underspecified research-method details for faithful implementation.

Researchers introduce IdeaAMBIG, a benchmark of 660 evidence-grounded instances (163 real-world gaps from reproducibility reports and GitHub issues, 497 controlled synthetic gaps) built from papers, codebases, and reproduction artifacts. It evaluates codification-readiness assessment, defect localization, and clarification action generation. Across 13 LLMs, the best model achieved only 9.6% Macro Defect Recovery Rate on real-world instances but 80.6% clarification success when given the annotated defect. An oracle study showed gold resolutions raise the codification-ready rate from 14% to 98%, identifying defect localization as the main bottleneck.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:59 UTC · 7d agoAI research2

AIUC Raises $40 Million to Certify Enterprise AI Agents

AIUC raised $40 million in Series A funding led by Ribbit Capital to expand its AIUC-1 standard certifying enterprise AI agents against security risks.

AIUC's Series A, led by Ribbit Capital with participation from First Harmonic, brings the company's total funding to $55 million. Its AIUC-1 standard tests AI agents against roughly 5,000 adversarial scenarios covering jailbreaks, prompt injections, hallucinations, anomalous behavior, and data leaks, with quarterly audits. Certified agents include Cursor, ElevenLabs, Fin, Harvey, KPMG, Lovable, and UiPath; the funds will extend audits, standards, and insurance to frontier models.

SecurityWeek·updated 18:07 UTC · 5h ago·first 13:38 UTC · 9h agoAI industry 2 sources

Operational Resilience: IT Security Risks with Reduced Staffing | Huntress

Huntress blog advises security teams on managing change, risk, and incident response during reduced-staffing holiday periods.

The article discusses how holiday-period staffing reductions change organizational risk profiles around change management, monitoring, and incident response capability. It argues against blanket change freezes when critical vulnerabilities with high exploitation probability demand patching, and stresses retaining decision-making authority, escalation paths, and recovery knowledge. It concludes by promoting Huntress Managed Response, which lets the Huntress SOC take predefined containment actions on confirmed threats without customer intervention.

Huntress·Sep 15, 14:00 UTC · 1d agoIndustry

Cybersecurity jobs available right now: January 20, 2026

Help Net Security·Aug 6, 12:24 UTC · Aug 6, 2026Vulnerability

Insights into Suspected DPRK Workers

Huntress details incidents involving suspected DPRK remote workers (Famous Chollima) in partner environments and shares detection indicators.

Huntress analyzed several incidents involving suspected North Korean remote workers, associated with the activity cluster known as Famous Chollima. The report describes indicators defenders can use to detect and prevent DPRK worker infiltration in customer environments. The scheme centers on operatives obtaining remote jobs at Western companies under assumed identities.

Huntress·Aug 26, 04:00 UTC · 21d agoThreat actor in the wild

Toward an Empirical Probabilistic Risk Manifestation Model of Organizational Cybersecurity in SMEs

Empirical study of 22 SME security assessments builds a probabilistic risk model and shows assessments can be cut 24-45% while retaining most critical findings.

Researchers analyzed 281 validated security findings from 22 real-world SME cybersecurity assessments conducted over two years via a pro bono university clinic. They derived an empirical Risk Manifestation Model linking eight organizational security functions to two exposure conditions, five attack mechanisms, and six outcome categories, using probability propagation to identify dominant risk pathways. The dominant pathway runs from asset exposure to credential compromise to unauthorized access, stable under leave-one-organization-out analysis. Retaining six functions reduces assessment burden by 24% while preserving 97% of critical findings; five functions cut burden 45% while preserving 89% of critical findings.

arXiv cs.CR·Sep 14, 01:15 UTC · 2d agoResearch

Engineered Persuasion: Evaluating Personalized Pretexts in LLM-Generated Spear Phishing

A study of 180 US workers found each LLM phishing personalization level raised click-intention odds by 28%, but credibility depends on context fit.

The arXiv paper evaluates how personalized pretexts in LLM-generated spear phishing affect perceived credibility, using 180 US working adults across 1,436 evaluations of emails with four cumulative personalization levels, from workplace context to shared-project details. Convincingness rose 2.40 points per level in sensitivity analysis and click-intention odds increased 28% per level, while non-clickers shifted toward deleting rather than reporting. Qualitative coding showed details matching the recipient's role and routines supported credibility, whereas incorrect, vague, or channel-inappropriate details raised suspicion. The authors argue personalization effectiveness depends on pretext fit, with implications for workplace security training.

arXiv cs.CR·Sep 3, 19:21 UTC · 13d agoResearch

PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving

PlannerForge unifies scenario-based testing of autonomous driving motion planners in one LLM-agent framework, outperforming prior baselines.

PlannerForge is an LLM-agent framework that covers the full scenario-based testing pipeline for autonomous driving systems, spanning scenario generation, selection, modification, routing, planner testing, plus new enhancement and benchmarking stages. In evaluations with 10 off-the-shelf LLMs, best-per-task scores range from 0.88 to 1.00, and open-source 20-35B backends such as Qwen3.6:35B match commercial APIs on most tasks. End-to-end chaining retains 83% (commercial) and 78% (open) of seed queries, beats Scenario Factory 2.0 on executable generation, and cost-tuning lifts planner success from 50.4% to 70.2% while cutting collisions from 19.0% to 8.4%.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research

Securing Your Business: The Vital Role of Cyber Insurance | Huntress

Huntress explains cyber insurance coverage types, insurer security requirements, and the shift toward documented evidence of controls.

Huntress outlines first-party and third-party cyber insurance coverage, including business interruption, data recovery, extortion, privacy liability, and regulatory fines. Insurers now commonly require EDR, MFA, security awareness training, patching, tested backups, least-privilege access, and incident response plans. With ransomware accounting for 91% of insurance losses in H1 2025 and average US breach costs at $10.22 million, underwriters increasingly demand evidence packs rather than self-attestation.

Huntress·Sep 1, 16:00 UTC · 15d agoIndustry

The gpg.fail aftermath: On responsible disclosure, GPG, and the state of security in 2026 [32:37]

A conference talk recounts GPG vulnerability disclosures, notes several GnuPG flaws remain unpatched, and demonstrates novel bugs live.

A researcher who disclosed multiple GnuPG vulnerabilities before 39c3 in December 2025 reports that several flaws, including one allowing spoofed PGP signatures, remain unpatched. Memory corruption in the basic PGP message parser was properly fixed, but GnuPG maintainer Werner Koch declared a widely-used feature 'harmful' instead of patching it. The talk presents additional novel GPG vulnerabilities and commentary on responsible disclosure and LLMs in security.

Lobsters · security·Sep 12, 17:24 UTC · 4d agoResearch

Product showcase: Trust Chain TPRM turns vendor compliance evidence into verified assurance

Help Net Security·Aug 10, 12:30 UTC · Aug 10, 2026Policy & legal

Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

New framework tests whether LLM-cited explanation factors are necessary or sufficient, finding weak correlation across Claude, GPT, and Gemini models.

An arXiv paper introduces black-box intervention tests measuring whether factors LLMs cite in their explanations are necessary or sufficient for their outputs in agent oversight workflows. Across eight models from the Claude, GPT, and Gemini families, Spearman correlations between cited rankings and measured influence ranged from 0.349-0.354 (advisor recommendation) to 0.431-0.580 (prompt monitoring). Uncited factors scored above the lowest cited factor in up to 57.6% of advisor responses, showing cited top-three factors do not reliably identify the most influential inputs.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:37 UTC · 12d agoAI safety & security1

Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation

A distillation framework compresses LLM reasoning into a 15.5M-parameter trade-up recommendation model reaching AUC 0.941 with product-type test-time training.

The paper targets trade-up recommendation, which identifies higher-quality alternatives that preserve customer purchase intent. A retrieval-augmented few-shot LLM teacher generates labels and rationales that supervise a compact embedding-pair classifier; at inference the 15.5M-parameter student uses only two precomputed 768-dimensional embeddings with no LLM calls. On 8,352 annotated pairs, label-only training scored AUC 0.912, reasoning distillation reached 0.924, and product-type test-time training lifted it to 0.941 with average precision 0.940. The distilled student is roughly 5,000x faster and 10,000x cheaper than direct LLM inference on a 100K-pair proxy catalog.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:08 UTC · 12d agoAI research

Scytale expands vendor risk management with AI-powered TPRM tools

Scytale launched AI-powered third-party risk management in its Vendors module, automating vendor discovery, risk scoring, and continuous vendor posture monitoring.

Scytale added AI-driven TPRM capabilities to its Vendors module, combining automatic vendor discovery from SSO providers and integrations with AI enrichment and dynamic risk scoring. The platform now continuously monitors vendors for breaches, data exposures, and vulnerabilities via third-party intelligence APIs, with proactive email notifications and auto-generated audit-ready security reports. It integrates with cross-framework control mapping for SOC 2, ISO 27001, GDPR, HIPAA, and SOX ITGC. Scytale cites Verizon's 2026 DBIR, which found 48% of breaches involved a third party, up 60% year over year.

Help Net Security·Sep 10, 10:22 UTC · 6d agoTools

GraphProfiler: Source-Linked Sensitive Attribute Inference via Personal Knowledge Graphs

GraphProfiler links LLM attribute inferences to source posts via personal knowledge graphs, enabling targeted redaction of privacy-leaking content.

GraphProfiler represents a user's post history as a source-linked personal knowledge graph where nodes and edges trace back to originating posts, making LLM-based attribute inference auditable. It reaches 86.7% attack success rate on the eight-attribute SynthPAI benchmark and 84.6% on PANDORA, within two points of strong text-only baselines, while citing supporting evidence for over 98% of predictions. Ablation experiments show removing cited posts reduces attack success substantially more than removing random posts, supporting targeted privacy mitigation.

arXiv cs.CR·Sep 11, 05:13 UTC · 5d agoResearch1

CVE-2026-73334: Apache Parquet Hadoop: File-controlled KMS URL is forwarded to pluggable KmsClient that skips host validation

Apache Parquet Hadoop CVE-2026-73334: a file-controlled KMS URL reaches pluggable KmsClients without host validation in parquet-java 1.12 through 1.18.0.

Apache disclosed CVE-2026-73334, a moderate issue in the org.apache.parquet.crypto.keytools package of parquet-java, versions 1.12 through 1.18.0. The package implements envelope encryption that wraps data keys via a Key Management Service. A KMS URL controlled by the Parquet file is forwarded to a pluggable KmsClient that skips host validation, which could allow crafted files to redirect KMS requests.

oss-security·Sep 8, 15:46 UTC · 8d agoVulnerabilityCVE-2026-733341

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·Sep 8, 20:00 UTC · 8d agoAI research

DPRK APTs: Ted backdoor and curlRAT target South Korean media and automotive sectors

Rapid7 uncovered a DPRK-linked Linux toolkit using a HAProxy-embedded ted backdoor, SSH keylogger, and curlRAT against South Korean media and automotive firms.

Rapid7 Labs identified a previously undocumented framework attributed with medium confidence to DPRK actors, targeting South Korean automotive and media organizations likely since early 2025. The toolkit embeds a backdoor compiled into HAProxy 2.8.12 using its filter API, plus trojanized crond, agetty, atd, sshd, and polkitd, an SSH keylogger storing credentials under /var/lib/sshd/, and a curl-based RAT with a watchdog thread. It enables remote command execution, malicious script injection into served webpages (a watering-hole loop), credential harvesting, and long-term surveillance. Hardcoded C2s are associated with APT37 via ThreatFox, and exposed groupware portals and mail servers align with Kimsuky tradecraft; the initial access vector and any CVE remain unconfirmed.

Rapid7 Blog·Sep 4, 12:00 UTC · 12d agoThreat actor in the wild1

RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments

RegionFed is a gradient-level federated learning framework enabling personalized retail query understanding while matching centralized accuracy with differential privacy.

RegionFed is an architecture-robust federated learning framework for personalized query understanding that operates at the gradient level, using the l2 conflict between regional and global gradients to diagnose heterogeneity and control personalization. Existing parameter-level personalized FL methods collapse on transformers, falling below 10% accuracy on T5, while RegionFed deploys unchanged on T5-Small, T5-3B, RoBERTa, and CNNs. RegionFed-Meta achieves 92.27% across Amazon ESCI, Amazon Reviews, and LEAF-FEMNIST, within 0.23 percentage points of the centralized upper bound, with epsilon-approx-0.60 differential privacy.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:50 UTC · 12d agoAI research1

PurpleDelta's Fraudulent Employment Operations

Recorded Future details North Korean cluster PurpleDelta using AI-generated personas and ChatGPT assistants to infiltrate companies via fraudulent employment.

Recorded Future profiles PurpleDelta, a North Korean IT worker threat cluster, in a new research report. The group uses AI-generated personas, sophisticated tradecraft, and custom ChatGPT assistants to obtain employment at target organizations and operate covertly. The report includes key indicators of compromise and recommended mitigation strategies for defenders.

Recorded Future·Aug 18, 00:00 UTC · 29d agoThreat actor

PrivAudit: A Dual-Lens Auditing Framework for Website Privacy Practices under the CCPA

PrivAudit framework audits 998 websites for CCPA compliance, finding stronger disclosures but pervasive, weakly responsive third-party cookie tracking.

PrivAudit is an automated dual-lens auditing framework combining LLM-based analysis of privacy policies grounded in CCPA provisions with automated browser measurements of cookie writes under diverse privacy configurations. Applied to 998 websites, it finds CCPA-subject sites disclose opt-outs, data sharing, and user rights more frequently, yet tracking remains pervasive: 6,392 targeting cookies, 49% third-party writes. Cookies show limited-to-moderate responsiveness to privacy signals and consent choices even when sites claim to honor them. The framework is open-sourced and shared with regulators.

arXiv cs.CR·Sep 9, 04:31 UTC · 7d agoResearch

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

Plug 'n' Pray: Agentic LLM-based Detection of Potential Log File Exposures in Third-Party Content Management System Plugins

Agentic LLM analysis validates 79 log file exposures across 62 of the 300 most-installed WordPress plugins, covering 250M+ active installations.

Researchers built an agentic LLM-based framework combining static and dynamic analysis to automatically detect insecure log files created by WordPress plugins. Scanning the 300 most-installed plugins, which account for roughly 75% of all active installations in the official ecosystem, it produced 81 findings with 79 manually reproduced across 62 plugins. Insufficiently secured log files can disclose credentials and personal data and have led to website compromises. The authors derive a taxonomy of log path and protection patterns and best practices, finding multi-layered protection often absent.

arXiv cs.CR·Sep 15, 13:29 UTC · 1d agoResearch