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Omniscience for the Masses: New Threats in the Metaverse's Democratized World Creation
First systematic assessment of 25 metaverse platforms reveals five novel world-creation attacks enabling covert user surveillance on Roblox, Horizon Worlds, and VRChat.
Researchers present the first systematic security and privacy assessment of metaverse world creators, surveying 25 platforms that support user-created worlds. They designed and implemented five novel attacks that abuse standard creator tools to violate spatial, visual, and auditory constraints, enabling covert user surveillance and manipulation without software vulnerabilities or developer-level privileges. Five previously proposed attacks were replicated using only standard world-creation features. The authors conclude that existing platform vetting, runtime protections, and creator policies are insufficient to mitigate malicious world creators.
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Heterogeneous Cross-Chain Transaction Tracing for Solana Bridges via Candidate-Set Selective Decision
SolTracer traces cross-chain transactions onto Solana bridges, improving open-world association F1 by 20.16% over the strongest baseline for illicit-fund tracing.
The paper formalizes four Solana-bound cross-chain transaction modes and proposes SolTracer, which maps heterogeneous execution semantics into a unified event space and uses candidate-set selective decision-making with abstention when valid targets are absent. In the challenging open-world setting with a 50% TA ratio, SolTracer improves F1 by 20.16% over the strongest baseline. An empirical study of real-world transfers examines count-value divergence across bridge mechanisms, cross-asset shifts, and decoupling between on-chain settlement and explorer visibility.
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Rare Not Random Using Token Efficiency for Secrets Scanning
Researcher proposes token efficiency (string length divided by BPE token count) as a better post-regex filter than entropy for secrets scanning, validated on CredData.
The post explores whether Byte-Pair Encoding tokenization can replace Shannon entropy as the primary filter for candidate secrets captured by regex in tools like Gitleaks. It defines 'token efficiency' as string length divided by token count under the cl100k_base tokenizer; secret-like strings such as GitHub tokens tokenize into many small tokens and score low, while natural text scores high. Evaluating labeled secrets from the CredData dataset shows a usable separation, with roughly 2.5 suggested as a minimum cutoff versus Gitleaks' 3.5 entropy threshold. The technique is positioned as a post-regex filtering step rather than a standalone detector.
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Efficient Branch-and-Bound Testing and Verification of zkVMs
ZEBRA verifies zkVM constraint systems via branch-and-bound cardinality counting, finding 11 zero-day bugs across five real-world zkVMs and running 51.5x faster than SMT verification.
ZEBRA reduces zkVM correctness to a solution-set cardinality problem requiring that each constraint system admit exactly one valid execution trace, eliminating redundancies like null-row padding and non-deterministic permutations before counting. It lifts analysis from finite-field witnesses to an integer interval lattice, exploiting that constraints across 5 real-world zkVMs use only 14.0% of theoretical connectivity capacity on average, enabling tight interval propagation. A parallel branch-and-bound search produces concrete counterexamples or certifies absence of violations within a bounded region. ZEBRA discovers 11 zero-day bugs (6 independently confirmed, 3 fixed), is 51.5x faster than SMT-based verification, and verifies 16.5 percentage points more instances.
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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.
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6 Months on Alert: Get H1 2026 Cyber Risk Report for SOCs and MSSPs
ANY.RUN's H1 2026 report details 15 threat trends including 437% growth in fake CAPTCHA phishing and 90.7% rise in Adobe infrastructure abuse.
The report draws on interactive sandbox submissions from over 700,000 analysts and 16,000 SOC teams between January and June 2026. Attacks abusing Adobe infrastructure grew 90.7% versus H2 2025 while RMM-related attacks rose 26.5%, and custom fake CAPTCHA phishing grew 437% from Q1 to Q2 2026. ANY.RUN argues static IOCs are losing effectiveness as dead drop resolvers hide the final C2 until execution.
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IntentFuzz: A Protocol-Aware Fuzzer for Automated Invariant Violation Detection in Intent-Based Cross-Chain Bridges
IntentFuzz protocol-aware fuzzer recovers bridge structure from unannotated Solidity and confirmed 22 invariant violations across 24 real-world deployments.
IntentFuzz formalizes a taxonomy separating invariant violations from settlement exposures in intent-based cross-chain bridges, then recovers a bridge's intent structure and deposit/fill function roles from unannotated Solidity source. It classified deposit and fill functions with 100% recall and 82% combined precision, and achieved 100% recall and precision on 23 planted-bug mutants. Across 24 real-world deployments it confirmed 17 genuine invariant violations with heuristic-only input generation, rising to 22 with its LLM-assisted tier, spanning eight vulnerable GitHub repositories with findings reproducible against public deployed bytecode.
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VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities
Introduces VEX-Bench, 75 expert-labeled real-world cases testing whether LLM agents can assess supply chain vulnerability exploitability; frontier models reach about 80% F1.
VEX-Bench is the first benchmark evaluating LLM agents on assessing whether upstream dependency vulnerabilities are exploitable in downstream projects, with 75 real-world expert-labeled cases across Python, Java, and Go mined from GitHub. Nine models across three agent harnesses were evaluated; GPT-5.5 and Claude Opus 4.6 reach approximately 80% F1 on binary vulnerability-status classification, but only GPT-5.5 surpasses 70% macro-F1 on fine-grained justification classification. The gap highlights the difficulty of moving beyond binary exploitability calls to explaining exploitability reasons, unlike prior benchmarks targeting zero-day settings.
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