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SpliTEE: Improving LLM Inference on Trusted Hardware with Differentially Private GPU Outsourcing
SpliTEE splits LLM inference between Intel TDX trusted execution and untrusted GPUs, using differential privacy instead of encryption to protect intermediate representations.
SpliTEE extends split inference to LLMs, running inference partly inside an Intel TDX TEE while masking intermediate inputs sent to untrusted GPUs with differential privacy rather than encryption. The authors show a prompt-reconstruction attack recovers nearly 80% of prompts from unmasked intermediate representations, motivating the masking. A global sensitivity analysis bounds the required DP noise scale, avoiding quantization and keeping models in floating point. The implementation is nearly twice as fast as full CPU-based TDX inference and 5-15 seconds faster than encryption-based Slalom with higher accuracy, evaluated on Llama-3.2-3B and Qwen3-4B.
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The modern attack chain: Rethinking Google Workspace security in the age of AI
Analysis of Vercel and Composio breaches shows stolen OAuth tokens, not phishing email, now open Google Workspace attacks — a path authorized AI agents follow.
The author analyzes the Vercel and Composio breaches as the same OAuth-first attack chain run twice: a stolen OAuth token, obtained via a compromised supplier, becomes the entry point rather than email. These tokens survive password resets, are hard to observe, and let attackers read Gmail and Drive data, take over accounts, and pivot laterally using stored credentials and password-reset magic links. The piece warns that authorized AI agents with overbroad OAuth grants can unintentionally traverse the same path — accessing inboxes, reading sensitive content, and exfiltrating data downstream — without any malicious actor or compromised credential.
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1Password's AI patching benchmark is misleading
Trail of Bits reanalysis says 1Password's 26% AI clean-fix rate is misleading; 86% of eligible patches blocked exploits.
Trail of Bits critiques 1Password's FLAWED AI patching benchmark, arguing its 26% clean-fix headline mixes trials where agents were instructed to apply wrong fixes (22% of data) with trials that prohibited compiling or testing (36%). Restricting to reasonable conditions, 2,634 of 3,067 patches (86%) blocked the supplied exploit. Trail of Bits also reports 12.5% of 2,265 developer first fixes failed in its own 2024-2026 assessments, and released post-patch-validation and review-walkthrough agent skills.
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I Am No One: Style-Aware Paraphrasing for Text Anonymization
Prompt-driven style-aware paraphrasing with LLMs cuts authorship attribution F1 by 60-70% while preserving content quality.
The paper proposes a style-aware, prompt-driven anonymization approach using pretrained LLMs to build compact stylistic profiles from minimal samples and rewrite text to suppress identifiable style markers while preserving meaning. It addresses stylometric re-identification risks in anonymized text, including ASR transcripts of meetings and call-center calls where leakage persists after acoustic anonymization. Across blog and review datasets, the approach reduces authorship attribution F1 by 60-70%, substantially outperforming both DP-based and non-DP baselines while maintaining readability.
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AutoTrans: AI-Assisted Automatic Translation of Security Assertions for RISC-V Processors
AutoTrans uses LLMs with regex extraction and formal verification to automatically translate security assertions across RISC-V processors, achieving 78% unattended acceptance.
AutoTrans is an automated framework for translating verified security assertions between RISC-V processor targets, where manual translation takes hours per assertion. It combines a regex-based SystemVerilog signal extractor to prevent LLM signal hallucination, a pinned prompt template yielding byte-identical prompts resilient to model updates, and JasperGold FPV formal verification of generated assertions. Applied with DeepSeek V4 to translate assertions between RISC-V targets such as IBEX and NS31A, it achieves a 78% automatic translation acceptance rate without human intervention and 100% after human refinement.
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The AI Malware Maturity Gap
Recorded Future introduces AIM3, a five-level maturity model for AI malware, showing current attacker AI use is mostly AI-assisted rather than autonomous.
Recorded Future proposes AIM3, a five-level model defining AI malware from LLM-translated to LLM-embedded, spanning experimentation to fully autonomous agentic campaigns. Public examples remain early-stage: PROMPTFLUX uses Google Gemini to rewrite its VBScript dropper (Level 1), while Lamehug/PROMPTSTEAL, attributed to APT28, invokes the HuggingFace API to generate reconnaissance commands (Level 3). The authors argue most current AI malware augments existing tradecraft rather than enabling one-click autonomous attacks.
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When the Whole Company Adopts AI: What It Does to Your SOC
Analysis of 16.9 million SOC alerts finds AI-related alerts at 0.43%, growing 685% since February, with 94.1% noise and 0.02% real attacks.
A review of roughly 16.9 million SOC alerts found about 73,000 (0.43%) were AI-related, a share that grew 685% between February and June 2026. Of AI-related alerts, 94.1% were noise, 5.8% genuine risks, and 0.02% real attacks; 79.8% received benign verdicts, 81.7% were automatically suppressed, and only 5.4% reached a human analyst. The only confirmed attacks were phishing campaigns that weaponized AI brand names as lures, while developer coding agents spawning shells and reading credential stores routinely tripped detections written before AI agents existed.
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A Graph-Based Approach for Mapping Kernel-Level Telemetry to MITRE ATT&CK
Trace2ATT&CK maps eBPF kernel telemetry to MITRE ATT&CK via provenance graphs and RAG with local open-weights LLMs, validated on 347 Atomic Red Team tests.
Trace2ATT&CK collects kernel-level events via eBPF, correlates attacker commands into a provenance graph, and derives compact graph representations suitable for LLM-based reasoning, mapping behavior to MITRE ATT&CK techniques with ranked candidates and rationales. Mapping uses both pure LLM prompting and retrieval-augmented generation grounded in the ATT&CK knowledge base. It was evaluated on 347 Linux Atomic Red Team tests using locally deployed open-weights LLMs. RAG consistently improved ATT&CK mapping over pure prompting, and provenance graphs substantially outperformed raw telemetry, without compromising data confidentiality.
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Credential Theft: How Attackers Steal & Use Stolen Credentials
Huntress explains how attackers steal credentials through phishing, AitM, infostealers, and dumping, then use them for lateral movement, BEC, and ransomware.
Huntress published an educational overview of credential theft, citing that roughly 70% of confirmed data breaches begin with stolen credentials. It details acquisition methods including phishing, adversary-in-the-middle attacks that capture MFA session tokens, infostealers (nearly a quarter of threats Huntress observed in 2025), Mimikatz-based credential dumping, credential stuffing, and password spraying. The piece then covers post-theft actions such as lateral movement, privilege escalation, account takeover, business email compromise, and ransomware, and closes with behavioral detection guidance and layered prevention strategies.
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Automobile Camouflage to Hide from Flock Cameras
Schneier on Security highlights a printed vehicle-camouflage pattern tested to defeat Flock surveillance cameras and Axon body cameras.
The post discusses covering cars with printed patterns designed to fool Flock automated license-plate recognition software, with testing reportedly done against Flock and Axon body cameras. Reader comments question effectiveness against other ALPR vendors, Flock's RF MAC-address upgrade, and whether such camouflage might become regulated. The page also contains off-topic comment threads about anti-bot over-blocking and privacy.
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Mapping out your unknown: A threat hunter’s guide to GitHub
Datadog Security Labs publishes a threat-hunting guide with audit-log queries to detect GitHub token theft, device code phishing, and source code exfiltration.
Datadog's threat-hunting guide covers GitHub audit log queries for detecting compromised accounts, stolen personal access tokens, and malicious OAuth app authorizations. Attackers typically obtain credentials through phishing, credential stuffing, leaked secrets, or device code phishing, then map private repositories, exfiltrate source code, and pivot into connected cloud and CI/CD environments. The guide maps detections to MITRE techniques like T1078 and T1528 and documents GitHub logging quirks affecting attribution, token metadata, and visibility fields.
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Bridging the First-Hour Gap: Evaluating AI Reliability and Benchmarking Deficiencies in Cyber Incident Response for Law Enforcement
Survey of playbooks, LLMs, RAG, and agentic AI for law-enforcement cyber first responders finds RAG most viable but benchmarks inadequate for legal requirements.
The paper surveys decision-support architectures (playbooks, LLMs, RAG frameworks, agentic AI) for frontline law enforcement during the first hour of a cyber incident, where volatile digital artifacts risk procedural errors and evidence attrition. RAG-based systems are identified as a relatively viable intermediate solution, though prompt sensitivity and confident hallucinations in legal contexts pose major risks. The authors find current cybersecurity benchmarks insufficient for law enforcement safety and legal demands, and argue for a new benchmark focused on naive query robustness and evidence preservation.
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An Open-Source End-to-End FHE Implementation for Privacy-Preserving Llama 3 8B Inference
Odin runs Llama-3-8B fully homomorphic encrypted inference on a single H100 in 366 seconds, a 4.51x speedup over THOR.
Odin is an open-source end-to-end GPU CKKS implementation for privacy-preserving Llama-3-8B inference that co-designs ciphertext packing with model execution. A feature-major cross-layer layout unifies residual connections and layer interfaces, while transient intra-operator layouts serve linear projections and attention, avoiding intermediate repacking of QK^T softmax outputs. Minimax polynomial approximation with input-range control reduces polynomial degree and multiplicative depth for nonlinear ops. With 128-token input, Odin evaluates all 32 Transformer layers on one NVIDIA H100 80 GB in 366.4 s using 58.9 GiB peak memory, versus 1651.9 s for the THOR baseline, a 4.51x speedup.
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Deep-Fake CAPTCHA: Mitigating Next-Generation Social Engineering Attacks
Researchers propose DF-CAPTCHA, a challenge-response defense that verifies callers in voice and video to defeat real-time deepfake impersonation in social engineering.
The DF-CAPTCHA framework prompts call participants with simple challenge-response tasks that are easy for humans but hard for real-time deepfake systems to convincingly generate. Responses are verified on four criteria: realism, identity consistency, task completion, and response time. User studies and experiments with real-time deepfake models across audio and video modalities show substantially improved detection over passive artifact-based methods.
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Subgroup Membership Inference Audits of Differentially Private Synthetic Text
Audits of 32 differentially private synthetic-text releases show subgroup membership leakage is concentrated in few records and systematically underestimated by average-case attacks.
The paper defines a subgroup-targeted membership inference game in which the target pool is an explicit parameter, to audit residual leakage in differentially private synthetic text releases. The audit instantiates 32 proxies across four datasets, three generators (DP-SGD fine-tuning, API-based prompting, and activation steering), and five privacy budgets. DP substantially reduces average leakage at every budget, but remaining leakage is concentrated: roughly a tenth of records carries about 40% of it, and the noise removes more measured leakage from random records than from high-risk ones. Which records leak depends on the release mechanism, so record-level risk cannot be assessed independently of the release.
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Why Johnny Can't Encrypt: A Usability Evaluation of PGP 5.0 (1999)
Seminal 1999 USENIX study finds most novice users cannot correctly sign and encrypt email with PGP 5.0 in 90 minutes.
Whitten and Tygar's USENIX Security Symposium paper evaluates whether cryptography novices can use PGP 5.0 effectively, using cognitive walkthrough analysis and a laboratory user test. The majority of test participants failed to successfully sign and encrypt a message within 90 minutes, despite PGP 5.0 having a well-regarded graphical interface. The authors argue that security requires usability standards beyond those of general consumer software and propose domain-specific UI design principles for security. The paper is a foundational reference in usable security research.
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LG smart TVs caught logging audio with screen off and snooping on local devices
Gamers Nexus found LG smart TVs record microphone audio in standby, scan home networks, and feed LG Ad Solutions ad targeting.
A 135-minute Gamers Nexus investigation with Level1Techs and independent researchers found retail LG OLED TVs running webOS sweep local networks, gather device names and Wi-Fi metadata, and run Automated Content Recognition. Tests showed the TVs capture clean microphone audio while appearing powered down and store it offline, uploading once reconnected. The team also found RCE vulnerabilities in webOS now moving through responsible disclosure; LG claims 216 million smart TV sales, and its ad unit claims access to 363 million addressable devices in the US.
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$536 and 8 Hours: AI Learns to Attack a Different PLC
Forescout used Claude to port a WAGO PLC exploit for $536 in 8 hours, and an AI-generated payload later permanently bricked the test device.
Forescout researchers used Claude Code with Ghidra, terminal access, and physical hardware to port CVE-2021-31886, a pre-authentication buffer overflow in the Nucleus FTP server, from a WAGO 750-852 PLC to the WAGO 750-831 without source code or debugger access. The final RCE development stage cost $535.74 in API fees over an 8-hour, 32-minute session (2.6k input and 1.3M output tokens) and required heavy researcher guidance. Claude produced working ICMP and UDP beacon exploits in 12 minutes after identifying that normal FTP command completion was overwriting its shellcode, fixed by omitting the CRLF terminator. A follow-up session extending the payloads toward a C2 implant wrote to flash-mapped memory and permanently bricked the PLC, underscoring the risks of autonomous agents operating against physical infrastructure.
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$20 per zero-day is already the WordPress plugin reality
TrendAI and CHT Security used an AI pipeline to find over 300 verified WordPress plugin zero-days at roughly $20 per vulnerability.
A pipeline built in three days by TrendAI and CHT Security, presented at Ekoparty Miami, paired AI-driven static analysis with automated Docker provisioning and Chrome DevTools MCP dynamic verification to surface more than 300 critical zero-days in WordPress plugins within 72 hours. The run consumed about 222 million tokens across 95 tasks, averaging roughly $20 per verified vulnerability, with findings including pre-auth RCE, SQL injection, privilege escalation, SSRF, and an AI-assembled downgrade attack chain. Dynamic verification eliminated over 80% of false positives, but manual review at 30-60 minutes per finding remains the bottleneck, straining ZDI and NIST triage backlogs.
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Zombie Card Attack Can Revive Expired Visa Cards for Contactless Payments
UMass Amherst researchers demonstrate Zombie Card, an NFC relay attack that revives expired Visa contactless cards for in-store purchases without breaking cryptography.
Researchers at the University of Massachusetts Amherst presented the Zombie Card attack at USENIX Security 2026, showing that Visa's Kernel 3 does not cryptographically bind the Application Expiration Date (tag 5F24) the terminal reads with the Track 2 expiry seen by the issuer. By positioning an NFC man-in-the-middle relay, an attacker can rewrite the terminal-facing expiration date of an expired card and complete contactless purchases, provided the account remains open under the same PAN and the bank does not independently re-check expiry. Testing across five major US banks found three distinct policies; Visa Kernel 3 accepted the modified date, while Mastercard, American Express, and Discover kernels declined modified transactions. Findings were disclosed to Visa and affected banks in May 2025, no CVE has been assigned, and no exploitation has been reported.
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Researchers find a loophole that lets expired credit cards make unauthorized payments
UMass Amherst researchers showed at USENIX Security 2026 that NFC relay attacks can rewrite unprotected expiration dates, letting expired Visa contactless cards keep making payments.
UMass Amherst researchers presented the Zombie Card attack at USENIX Security 2026, using two smartphones to relay and modify NFC payment data from expired Visa cards. The expiration date lies outside the card's digital signature coverage in Visa Kernel 3, and one major US bank accepted $1-$500 test purchases plus live $2.79 and $3.19 purchases with rewritten dates. Mastercard, American Express, and Discover rejected tampered dates; researchers notified Visa in May 2025 and no CVE or confirmed fix existed at publication.
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Google Password Manager Attacks Could Let Malware Hijack Passkey
Unit 42 details three attack paths letting Windows malware silently sign into passkey-protected accounts via Chrome's Google Password Manager without user verification.
Palo Alto Networks Unit 42 described three post-compromise attack paths—Pass-ta-key, Silver Pass-ta-key and Golden Pass-ta-key—against Chrome's Google Password Manager cloud authenticator on TPM-equipped Windows systems. The attacks can silently obtain valid authentication assertions, install attacker-controlled user-verification keys, or extract the 32-byte Security Domain Secret used to decrypt synced passkey private keys, enabling reusable access from the attacker's own environment. No CVE was assigned and no exploitation in the wild was reported; demonstrations were validated against Chrome 142 and parts of the architecture are corroborated by Chromium source. GitHub enforced the User Verified flag check, while eBay accepted a test assertion lacking it before fixing its validation gap after disclosure.
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