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SEMA-GUARD: Semantic and Graph-Based Vulnerability Detection in Assembly Code
SEMA-GUARD uses semantic analysis and graph neural networks to detect vulnerabilities in assembly code, achieving 85.1% accuracy on a Juliet-derived benchmark.
SEMA-GUARD is a framework that detects vulnerabilities in compiled programs when source code is unavailable, targeting malware, firmware, and embedded systems analysis. It enriches control flow graphs with low-level execution semantics including stack manipulations, memory accesses, and data flow. Evaluated on a Juliet Test Suite set compiled to assembly and split into function-level chunks, it achieves 85.1% accuracy and an F1 score of 0.801, outperforming purely statistical or structural approaches.
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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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Can your coding style predict whether your code is vulnerable?
University of Massachusetts Dartmouth researchers present VulStyle, a stylometry-based vulnerability detector that also exposes benchmark reliability problems.
VulStyle combines stylometric features with syntax-tree structure and source tokens, pre-trained on about 4.9 million functions across seven programming languages and fine-tuned on five vulnerability detection datasets. It beat token-only detectors on some benchmarks but its F1 drops sharply on DiverseVul, which the authors link to noisy labels inflating reported performance across popular datasets. The authors argue style-aware detection should be harder to evade but did not test this empirically, and they note that uniform LLM-generated code may strip away the individual developer style the model depends on.
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Risky Bulletin: Academics find source code overlaps between Geedge and China's Great Firewall
Academics linked Chinese vendor Geedge Networks' Tiangou Secure Gateway source code to one of the Great Firewall's three traffic filtering capabilities.
US researchers presenting at USENIX Security reconstructed Geedge Networks' Tiangou Secure Gateway firmware from over 100,000 leaked files, including Git repositories with commit history, and matched its filtering behavior to sections of China's Great Firewall. They found only 1 of 3 characterized DNS injectors matched Geedge code, noted the system relies on memory-unsafe C components and copied third-party code, and said its bugs could aid future circumvention tools. Geedge also exports censorship tools to Kazakhstan, Ethiopia, Pakistan, and Myanmar. The newsletter additionally rounds up multiple breaches.
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SCHERI: Provably Secure Speculation Under the Constant-Time Policy for CHERI (Extended Version)
Researchers formally prove existing CHERI speculation proposals leak secrets and present SCHERI, a processor design with end-to-end Spectre-resistant constant-time guarantees.
The paper builds a formal framework reasoning jointly about capability safety, speculative execution, and information-flow security on CHERI architectures. It demonstrates that existing secure-speculation proposals fail to preserve constant-time confidentiality guarantees and can transiently leak isolated secrets. The authors present SCHERI, a new processor design formally proven to provide end-to-end secure speculation for the constant-time policy, resilient to Spectre attacks.
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GAUGE: A Formal Framework for Measuring Cryptographic Security under Heterogeneous Adversary Cost Models
GAUGE frames cryptographic security as profiles over adversary cost models, certifying a ranking reversal between ML-KEM-512 and AES-128 from a 4–5% memory pricing shift.
GAUGE represents cryptographic security as a function over admissible adversary cost models (a security profile), proves profiles are piecewise-linear and concave, and establishes a rating trilemma when two profiles cross. A polynomial-time linear-programming procedure certifies whether the ranking of two schemes is robust, reverses under admissible models, or is genuinely incomparable. Applied to NIST post-quantum standards, the framework certifies a ML-KEM-512 versus AES-128 ranking reversal from a 4–5% shift in memory pricing and measures lattice-sieving cost drift of 9.79 bits per year over eight years. A hybrid X25519 + ML-KEM-768 handshake reduces combined-break probability twenty-fold at a 2.3 kilobyte cost.
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How Attackers Abuse VSS, and How Huntress Detects It
Huntress details how attackers abuse Windows Volume Shadow Copies for ransomware recovery sabotage and NTDS.dit credential theft, plus detection logic.
Huntress explains that attackers abuse VSS in three ways: deleting shadow copies to inhibit recovery before ransomware detonation, creating shadow copies to extract the NTDS.dit Active Directory database for offline credential theft, and manipulating shadow copy configuration. Because backup agents and RMM tools routinely create and delete shadow copies, raw events are too noisy to alert on alone. Huntress detections instead correlate VSS activity with lateral movement and credential harvesting over a time window, such as an observed sequence of PsExec spawning SYSTEM shells on a domain controller, vssadmin create shadow, a blocked deletion attempt, and DNS reconnaissance against another host.
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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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PHAT: PHotonic Accelerator for TFHE
PHAT photonic accelerator using optically addressed phase-change memory speeds TFHE bootstrapping FFTs 2.14x-5.10x over the prior ASIC accelerator.
Researchers propose PHAT, an electro-photonic accelerator for TFHE fully homomorphic encryption built on optically addressed phase-change memory (OPCM) processing-in-memory. The architecture adds OPCM-based FFT units, a twiddle-stationary dataflow, and a scheduling mechanism to maximize FFT utilization for TFHE bootstrapping. It achieves 2.14x-5.10x speedups across four real-world TFHE workloads versus the state-of-the-art ASIC accelerator, improving feasibility of privacy-preserving computation in the cloud.
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On Identifying Sound Conditions for Frontrunning Resistance
Researchers formally define smart-contract frontrunning resistance, showing 55% of 393 audited vulnerabilities escape state-of-the-art detection, and find two undisclosed Ethereum flaws.
The paper gives the first formal definition of frontrunning vulnerability for smart contracts, grounded in how honest users interact with contracts rather than contract code alone. In a large-scale study of 287 smart contract audits, 55% of the 393 vulnerabilities reported by leading auditors fall outside the scope of state-of-the-art dynamic detection criteria. The authors present a sound algorithm for synthesizing secure interaction conditions and apply it to real-world contracts, uncovering previously undiscovered vulnerabilities in two Ethereum contracts.
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Getting ahead of ‘harvest-now-decrypt-later’: Post-quantum cryptography planning
Opinion piece urges organizations to begin post-quantum cryptography migration now, citing harvest-now-decrypt-later risk and NIST deadlines.
CSO Online outlines why harvest-now-decrypt-later makes long-lived sensitive data a current risk even before quantum computers exist. It cites NIST IR 8547 timelines deprecating RSA-2048 and ECC P-256 by 2030 and removing them by 2035, finalized FIPS standards ML-KEM, ML-DSA, and SLH-DSA, upcoming FN-DSA (FIPS 206), NSA requirements for national security systems from 2027, and UK NCSC phased guidance through 2035. The author recommends cryptographic discovery, crypto-agility, and prioritizing long-confidentiality data and TLS endpoints.
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Towards Standardized Evaluation of GPU Memory Safety with GMSBench
GMSBench provides 149 CUDA tests covering spatial, temporal, and concurrency GPU memory errors, exposing detection gaps in Compute Sanitizer.
GMSBench is a GPU memory safety benchmark comprising 149 self-contained CUDA tests spanning spatial, temporal, and concurrency errors across different GPU memory spaces and execution scenarios. The authors evaluate NVIDIA's Compute Sanitizer across multiple GPU architectures using the suite, exposing gaps in its detection coverage. The benchmark offers a standardized foundation for comparative evaluation of GPU memory safety mechanisms.
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Trusting-Trust Attack against an Entire Linux Distribution (via the strip utility)
ArXiv paper shows the trusting-trust compiler backdoor technique can compromise an entire Linux distribution via the strip utility.
The paper (arXiv 2607.24888) demonstrates that Ken Thompson's trusting-trust attack, long viewed as a compiler-specific threat, can backdoor an entire Linux distribution by targeting the strip utility. A compromised tool reproduces its backdoor in subsequent rebuilds of itself, generalizing the attack surface beyond compilers. The finding has supply-chain implications for build reproducibility and distribution trust, though it is a research result with no observed real-world exploitation.
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Researchers Use Claude to Port Pre-Auth RCE Exploit From One PLC Model to Another
Forescout used Anthropic's Claude to port a working pre-auth RCE exploit for CVE-2021-31886 between WAGO PLCs, executing ARM shellcode on live hardware.
Forescout Vedere Labs used Claude interactively to port an RCE exploit for CVE-2021-31886, a CVSS 9.8 stack buffer overflow in the Nucleus FTP server's USER command, from a WAGO 750-852 to a WAGO 750-831 PLC, running attacker-supplied ARM shellcode. The final RCE stage cost $535.74 in API usage over 8 hours 32 minutes, and a follow-up attempt to build a C2 implant permanently bricked the device. CERT@VDE lists many Nucleus V1-based WAGO models as vulnerable with no updates available; Siemens plans no Nucleus NET remediation. The work follows a joint NSA/CISA/FBI/DOE/EPA advisory warning of AI-generated exploitation scripts targeting internet-exposed Siemens S7 PLCs.
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Why The Vulnerability Backlog Is About To Get Worse
Recorded Future analysis says AI-driven vulnerability discovery and faster weaponization will grow the triage backlog while shrinking defenders' response windows.
Disclosed vulnerabilities rose from roughly 21,000 in 2021 to nearly 50,000 in 2025, while Recorded Future assessed only 446 as actively exploited in 2025. VulnCheck found nearly 29% of 2025 KEV entries were exploited on or before CVE publication. The authors argue AI-assisted discovery and automated exploit development will multiply credible reports, cut disclosure-to-exploit time toward minutes, and force re-evaluation of medium-severity flaws as exploit-chain components.
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Code review used to be the only way to catch these bugs
Palo Alto Networks' Unit 42 says its NOVA system found 14,090 vulnerabilities in 3,915 open-source projects, mostly non-crashing bugs like access control flaws.
Unit 42's NOVA system analyzed 3,915 open-source projects over two months and reported 14,090 validated vulnerabilities, only 85 of which matched previously documented findings. 92% of findings fell outside fuzzing-friendly categories, clustering instead in access control, path traversal, injection, prototype pollution, and SSRF; language ecosystems showed distinct weakness profiles. Of 5,421 supply-chain findings, 1,280 were flaws in dependencies while 4,141 were downstream exposures, 2,776 validated with working proof-of-concepts. Unit 42 warned that faster discovery combined with an average 55-day patch deployment window has collapsed the patch-to-exploit gap.
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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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New Report: AI threats are here. Why Q2 2026 signals the end of traditional patch cycles
Rapid7 Labs' Q2 2026 threat report finds vulnerability disclosures surging while AI-assisted attackers compress the time from disclosure to exploitation.
Rapid7 Labs' Quarterly Threat Landscape Report for Q2 2026 reports continued growth in vulnerability disclosures alongside attacker use of automation and AI-assisted tooling. The report argues the window between disclosure and exploitation is shrinking, eroding the value of traditional patch cycles. It recommends prioritizing exposures attackers can actually reach rather than attempting to patch everything.
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Autoencoder Is All You Need: Profiling and Detecting Malicious DNS Traffic
Palo Alto Unit 42 details an autoencoder-based method that profiles DNS traffic to detect C2 and malicious domains, blocking ~374,000 malicious DNS requests daily.
Unit 42 built an RNN-based autoencoder that compresses DNS traffic time series into fixed-dimensional 'DNS profiles' for each domain and device. Downstream classification, clustering, and anomaly detection modules flag suspicious domains, capturing 170 emerging suspicious domains in May 2024. Signatures block roughly 374,000 malicious DNS requests daily and run in the Advanced DNS Security service, with detections shared to Advanced URL Filtering. Case studies link DNS traffic patterns to C2 beaconing, dynamic DNS abuse, and DNS tunneling for data exfiltration.
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Attack Paths Into VMs in the Cloud
Unit 42 maps attack paths into AWS, Azure, and GCP VMs through intended features like startup scripts and SSH key pushes.
Palo Alto Unit 42 reviewed attack vectors against virtual machine services on AWS, Azure, and GCP, finding that 11% of internet-exposed cloud hosts carry Critical or High severity vulnerabilities. The attack paths rely on legitimate features such as EC2 User Data, VM custom data, EC2 Instance Connect, SSM Run Command, and serial consoles rather than vulnerabilities, and exploiting them requires attackers to first obtain control plane permissions. A compromised VM exposes not only its data but the workload identity and cloud permissions assigned to it, making identity compromise potentially more damaging than data theft. The firm places mitigation responsibility on cloud users and administrators.
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338 Million Attack Simulations Reveal The State Of Enterprise Defense
Picus Labs' Blue Report 2026, from 338 million attack simulations, finds defenses strong at the perimeter but blocking only 37% of post-compromise actions.
Picus Labs' fourth annual Blue Report analyzed over 338 million attack simulations from production environments in H1 2026. Average prevention effectiveness rose from 62% to 69%, but only 37% of attacker actions were blocked after compromise, with reconnaissance and credential theft largely missed. IOC-based malware download prevention fell to 50% from 71% in 2024, and Mimikatz credential dumping from LSASS memory was blocked 94% of the time versus 17% from other memory locations and 3% from registry.
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