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ROSETTA: Efficient and Accurate Privacy-Preserving LLM Decoding via Hybrid CKKS/TFHE Evaluation
ROSETTA is a hybrid CKKS/TFHE homomorphic encryption framework for privacy-preserving LLM decoding, achieving up to 4.8x Softmax and 2.1x end-to-end speedups.
The paper proposes ROSETTA, a hybrid CKKS/TFHE fully homomorphic encryption framework for private inference on generative LLMs, targeting the nonlinear operations that dominate autoregressive decoding cost. It introduces an adaptive segmented lookup-table protocol based on TFHE and a scheme-aware operator-selection framework that assigns each nonlinear operator to CKKS or TFHE to minimize latency. Experiments show up to 4.8x Softmax speedup and 1.5-2.1x end-to-end decoding speedup over the state-of-the-art CacheMir framework.
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Shuffling is Not Enough: Breaking Permutation-Based Model Confidentiality in Hybrid FHE Inference
Attack breaks permutation-based model confidentiality in hybrid FHE inference, recovering all ResNet-20 linear layers exactly with d+1 queries per layer.
The paper shows output-permutation plus noise fails to protect model confidentiality in hybrid FHE inference: d+1 admissible queries recover an exact permutation-invariant summary of a d-input linear layer, and shuffle-model DP amplification premises cannot hold under correctness-bounded noise. The authors recovered all linear layers of a Safhire-style ResNet-20 end-to-end from TFHE transcripts with zero error, using 5,712 total queries. Exact per-layer recovery was also confirmed on pretrained ImageNet-scale CNNs and ViT-B/16. Leaked layer spectra enable model fingerprinting, lineage attribution, and improved logit-based extraction, while suppressing them destroys inference utility.
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Observational Indistinguishability and Integrity Blind Regions in Hybrid Quantum-Classical Workflows
Framework formalizes integrity blind regions in hybrid quantum-classical workflows, validated across 3,600 label interventions with conformal detection rules.
The paper presents a claim-relative evidence and reference framework for integrity of hybrid quantum-classical workflows, distinguishing structural blind regions caused by observational indistinguishability from finite-batch statistical misses. Experiments over 3,600 label interventions show exact label-path invariance for feature and prediction views. The geometry-aligned construction detects 343 of 2,700 conclusion-changing interventions using the conformal rule and 1,183 of 2,700 with the uncorrected union, with executed conformal clean false-action rates of 0.048-0.059.
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Automatic Key Exchange: faster, post-quantum secure origin handshakes for 45 billion daily connections (and counting)
Cloudflare's Automatic Key Exchange probes origins to lead with preferred key exchange, cutting HelloRetryRequests from 52% to 3.7% and enabling automatic post-quantum origin connections.
Cloudflare announced Automatic Key Exchange, an extension of Automatic SSL/TLS that probes each origin's supported key agreement algorithms and leads with the preferred one, favoring the post-quantum hybrid X25519MLKEM768. Rollover across roughly 45 billion daily origin connections cut HelloRetryRequests from about 52% to 3.7%, saving more than 150 ms of p90 handshake latency. Hundreds of thousands of domains now receive post-quantum origin connections without manual configuration, supporting Cloudflare's goal of quantum-safe encryption by 2029 to counter harvest-now-decrypt-later attacks.
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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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Event-Native Symbolic-Temporal Spike Encoding Framework for Heterogeneous Cyber Streams
An event-native spike encoding framework lets recurrent spiking neural networks run intrusion detection directly on heterogeneous packet and CAN bus streams.
The paper maps heterogeneous cyber events—categorical identifiers, local frequency context, and inter-event timing—directly into sparse spike-compatible inputs for spiking neural networks. This avoids flow aggregation and fixed windows that add buffering latency and obscure temporal structure in traditional IDS pipelines. Compact recurrent SNNs under edge-oriented neuromorphic hardware constraints achieve a hybrid anomaly metric of 0.987 on packet-level Network IDS and 0.980 on message-level CAN IDS.
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Evidence-Grounded Retrieval for Investigation Hunt Lead Generation from CTI Reports
AHLERT generates environment-aware threat hunting leads from CTI reports using ATT&CK-seeded knowledge graph retrieval, roughly doubling F1 over flat-RAG baselines.
The paper introduces AHLERT, a system that converts Cyber Threat Intelligence reports into structured, investigable hunt leads via hybrid dense retrieval with multi-hop traversal over an MITRE ATT&CK-seeded knowledge graph and ontology-grounded RAG constrained to the defender's assets. It is LLM-agnostic and evaluated on public CTI reports for well-known APTs across proprietary and open-weight models. Hybrid evidence retrieval with ontology grounding raises mean F1 from 0.44 to 0.85, and AHLERT attains the highest effectiveness score (~86.95%) versus off-the-shelf LLMs.
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A GAN-Based Framework for Robust DDoS Attack Detection
WGAN-GP-generated adversarial DDoS traffic augments training data, improving detection resilience against evasion attempts.
Researchers built a DDoS detection framework combining Random Forests, deep neural ensembles, and Transformer-based models trained on CICDDoS2019 with synthetic adversarial flows generated by a Wasserstein GAN with gradient penalty. Hybrid datasets of benign, malicious, and generated traffic taught models more generalizable decision boundaries. Experiments showed improved accuracy and resilience against unseen adversarial traffic, validated on real-world generated flows.
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Apple Reference Image: A New Approach for Verified Photography
Apple introduces Reference Image, hardware-backed verifiable photography on iPhone 18 Pro using sensor signing and Private Cloud Compute to counter AI-generated fakes.
Apple announced Reference Image, an opt-in camera mode debuting on the main sensor of iPhone 18 Pro and iPhone 18 Pro Max that produces securely timestamped, verifiable photographs. The design splits into two phases: a secure digital negative created by cryptographically signing pixel data at the sensor immediately after capture (preventing injection or tampering), then developing that negative into a reference image. Private Cloud Compute handles processing without exposing image contents to anyone, including Apple, and fraudulent reference images can be revoked without revealing the photographer's identity. Apple positions the system as stronger than C2PA-based approaches, which sign metadata after capture, are vulnerable to editing-chain compromise, and can tie images to a device or individual.
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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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OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design
OptiPrime co-designs HE-MPC protocols with hardware acceleration to remove network communication bottlenecks in private DNN inference, beating Cheetah by up to 5.7x.
OptiPrime is a protocol-hardware co-optimization framework for private deep neural network inference based on hybrid homomorphic encryption (HE) and multi-party computation (MPC). It introduces a novel HE protocol for convolutions that reduces the number of transmitted output ciphertexts, addressing the network bottleneck that limits gains from commercial HE accelerators. A lightweight compression system reduces weight plaintext memory traffic by 10x, while a specialized dataflow maximizes on-chip reuse of intermediate ciphertexts. Experiments show up to 5.7x speedup over the Cheetah baseline on CPUs and 4.2x with an accelerator.
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Automating Attack Graph Construction for Agentic Pentesting. Towards Neuro-Symbolic Vulnerability Hunting
Semi-automated pipeline converts Trivy, Semgrep, Nmap output into MulVAL attack graphs for agentic pentesting, 53.7% mean vulnerability coverage in CyBench.
The paper presents a semi-automated pipeline that parses Trivy, Semgrep, and Nmap findings into MulVAL predicates and uses an LLM-assisted process to build domain-specific Datalog rules linking scanner evidence to attack techniques. MulVAL/XSB then performs symbolic inference to generate structured, auditable attack paths for agentic pentesting. Evaluated on 54 web CTF tasks from CyBench, every task produced at least one goal-reaching graph with 53.7% mean ground-truth vulnerability coverage, 51.9% full coverage, and an 83.9% noise-path rate. Median end-to-end runtime was 24.9 seconds, making the pipeline runtime-practical for agentic workflows.
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PDoS: A Profitable Denial-of-Service Attack against Proof-of-Work Blockchain Liveness
PDoS attack disrupts Proof-of-Work blockchain liveness profitably by combining miner deterrence signals with parasitic revenue extraction from victim pools.
PDoS is a hybrid incentive-driven denial-of-service attack that combines block header signal deterrence with parasitic revenue extraction from a victim pool's share-reward mechanism. By subsidizing attack costs through the victim pool, it lowers the hash-power threshold required to induce rational miners to shut down, unlike prior attacks such as BDoS that demand sustained attacker losses. The authors show a counterintuitive result: in high-fee or high-MEV environments, higher block value increases the attacker's parasitic revenue and can push the attack past break-even into a self-sustaining or even profitable regime. PDoS is claimed as the first attack demonstrating that disrupting PoW blockchain liveness can be economically self-sustaining.
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You Get What You Sample: Evaluating Sampling Strategies for Web Security Measurements
Evaluation of 500k Tranco and 24.8M Common Crawl hosts shows Top-N domain sampling biases web security measurements; probability sampling yields unbiased estimates.
The study is the first comprehensive investigation of how sampling strategies affect web security measurement conclusions, comparing datasets and strategies across 500k Tranco domains and 24.8M Common Crawl hosts. It shows Top-N selection does not reflect the overall web distribution and may bias observed vulnerability rates, while probability-based strategies yield stable, unbiased prevalence and impact estimates. Hybrid sampling offers no advantage because its deterministic prefix consistently hurts accuracy, and the authors propose an adaptive probability-based strategy effective even when target prevalence is unknown.
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Operational Roles of QRNG-Derived Quantum Entropy in Bitcoin Proof-of-Work Architectures
arXiv study finds quantum-random entropy adds no Bitcoin PoW success advantage but helps assurance in fault and provenance scenarios.
The paper shows replacing classical entropy with QRNG output does not change honest Bitcoin proof-of-work success probability when candidate headers remain distinct. It introduces a reproducible benchmark measuring an entropy-efficiency factor and a reboot-diversity index, finding QRNG value only in assurance-oriented scenarios involving correlated restart faults, namespace reuse, and entropy provenance. Validation is simulation-based, with hardware-in-the-loop testing identified as future work.
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