k-REWB matching cannot be solved in O(n to the 2k minus epsilon) time under SETH, is W[2]-hard parameterized by expression length, and 2-use 2-REWBs require superlinear time unless triangle detection does; 1-use REWBs admit an O(n log squared n) algorithm.
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Proceedings of the 42nd IEEE Symposium on Security and Privacy (SP) , year =
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A general framework defends any shuffle-DP protocol against poisoning attacks on union-preserving queries, retaining asymptotically equivalent error without attacks and only polylogarithmic increase with constant attackers.
SuperDP refutes ε-DP via simultaneous synthesis of input pairs and witness functions using upper expectation supermartingales and lower expectation submartingales, delivering the first fully automated, sound, and semi-complete method applicable to both discrete and continuous stochastic mechanisms.
A game semantics framework delivers the first sound and complete open-world assertion checking for Ethereum smart contracts, implemented as YulTracer which achieves perfect recall and precision on reentrancy benchmarks and real-world exploits.
OverrideFuzz uses semantic-aware grammar fuzzing with reflection to model override hooks and dynamic rebinding, producing coverage growth and inputs that match known vulnerability patterns on CPython, Lua, and QuickJS without discovering new bugs in the evaluation window.
A low-stake adversary can degrade a liquid staking pool's performance via consensus manipulation and profit from the resulting drop in its LST value through application-layer financial positions.
Constructs a logical-relations security model for where-declassification in higher-order languages by halting indistinguishability enforcement after relevant declassifications, yielding stronger guarantees than prior lower-order definitions.
A taxonomy of GitHub abuse behaviors is proposed along with a detection framework achieving F1-scores exceeding 89% on a manually labeled dataset of 392 instances.
Privatar uses horizontal frequency partitioning and distribution-aware minimal perturbation to enable private offloading of VR avatar reconstruction, supporting 2.37x more users with modest overhead.
Semantic typing via coinductively defined interpretations on a typed operational semantics ensures information flow control and non-interference for TinySol contracts that use fallback functions.
Persistent BitTorrent Trackers use smart contracts and aggregated cryptographic attestations to make reputation verifiable and resilient to tracker shutdowns, with a hybrid signature scheme and authenticated DHT fallback for decentralized access control.
Swapping the reasoning trace prefill on unlearned weights can replicate or reverse the parser-split bypass gap, showing that the gap alone does not identify or rule out weight-level memorization.
Interpretability research should be judged by actionability—the degree to which its insights support concrete decisions and interventions—rather than explanatory power alone.
EASE closes three residual anchors in federated multimodal unlearning using bilateral displacement, cosine-sine decomposition, and forget lock, achieving near-retrain performance on forget and retain data.
CuLifter recovers types from untyped GPU register files via constraint propagation to lift 99.98% of 24,437 functions across 919 cubins to valid LLVM IR.
An encoding of Solidity contracts and first-order Hennessy-Milner logic into Lustre enables Kind 2 model checking of complex temporal properties in smart contracts.
Sparse Concept Anchoring biases neural latent spaces toward targeted concepts using under 0.1% labels per concept, enabling reversible steering via projection and permanent removal via weight ablation with minimal side effects on other features.
Structured CTI standards like ATT&CK describe adversary actions but lack the ordering, preconditions, and environmental details needed for direct multi-stage emulation, and a translation method can bridge this gap when assumptions are recorded.
Approximate subject-level unlearning recovers 89.3% and 92.5% of oracle performance gains on EngageNet and DAiSEE at roughly one-quarter the retraining cost in K=3 forget-set regimes.
Augmenting binaries with compilation metadata makes disassembly decidable, enables correct lifting to recompilable representations, and improves analysis reliability while adding negligible size and no runtime overhead.
Generative AI boosts attackers' ability to create harmful content at scale while also enabling defenders to detect threats, support users, and improve moderation processes.
citing papers explorer
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On the Complexity of the Matching Problem of Regular Expressions with Backreferences
k-REWB matching cannot be solved in O(n to the 2k minus epsilon) time under SETH, is W[2]-hard parameterized by expression length, and 2-use 2-REWBs require superlinear time unless triangle detection does; 1-use REWBs admit an O(n log squared n) algorithm.
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Defense against Poisoning Attacks under Shuffle-DP
A general framework defends any shuffle-DP protocol against poisoning attacks on union-preserving queries, retaining asymptotically equivalent error without attacks and only polylogarithmic increase with constant attackers.
-
SuperDP: Differential Privacy Refutation via Supermartingales
SuperDP refutes ε-DP via simultaneous synthesis of input pairs and witness functions using upper expectation supermartingales and lower expectation submartingales, delivering the first fully automated, sound, and semi-complete method applicable to both discrete and continuous stochastic mechanisms.
-
Open-World Assertion Checking for Smart Contracts via Game Semantics
A game semantics framework delivers the first sound and complete open-world assertion checking for Ethereum smart contracts, implemented as YulTracer which achieves perfect recall and precision on reentrancy benchmarks and real-world exploits.
-
OverrideFuzz: Semantic-Aware Grammar Fuzzing for Script-Runtime Vulnerabilities
OverrideFuzz uses semantic-aware grammar fuzzing with reflection to model override hooks and dynamic rebinding, producing coverage growth and inputs that match known vulnerability patterns on CPython, Lua, and QuickJS without discovering new bugs in the evaluation window.
-
Your Loss is My Gain: Low Stake Attacks on Liquid Staking Pools
A low-stake adversary can degrade a liquid staking pool's performance via consensus manipulation and profit from the resulting drop in its LST value through application-layer financial positions.
-
Compositional security definitions for higher-order where declassification
Constructs a logical-relations security model for where-declassification in higher-order languages by halting indistinguishability enforcement after relevant declassifications, yielding stronger guarantees than prior lower-order definitions.
-
Weaponizing the Commons: A Taxonomy and Detection Framework of Abuse on GitHub
A taxonomy of GitHub abuse behaviors is proposed along with a detection framework achieving F1-scores exceeding 89% on a manually labeled dataset of 392 instances.
-
Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading
Privatar uses horizontal frequency partitioning and distribution-aware minimal perturbation to enable private offloading of VR avatar reconstruction, supporting 2.37x more users with modest overhead.
-
Typing Fallback Functions: A Semantic Approach to Type Safe Smart Contracts
Semantic typing via coinductively defined interpretations on a typed operational semantics ensures information flow control and non-interference for TinySol contracts that use fallback functions.
-
Persistent BitTorrent Trackers
Persistent BitTorrent Trackers use smart contracts and aggregated cryptographic attestations to make reputation verifiable and resilient to tracker shutdowns, with a hybrid signature scheme and authenticated DHT fallback for decentralized access control.
-
Auditing Reasoning-Trace Memorization Claims after Unlearning with Head-Conditioned Canaries
Swapping the reasoning trace prefill on unlearned weights can replicate or reverse the parser-split bypass gap, showing that the gap alone does not identify or rule out weight-level memorization.
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Interpretability Can Be Actionable
Interpretability research should be judged by actionability—the degree to which its insights support concrete decisions and interventions—rather than explanatory power alone.
-
EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure
EASE closes three residual anchors in federated multimodal unlearning using bilateral displacement, cosine-sine decomposition, and forget lock, achieving near-retrain performance on forget and retain data.
-
CuLifter: Lifting GPU Binaries to Typed IR
CuLifter recovers types from untyped GPU register files via constraint propagation to lift 99.98% of 24,437 functions across 919 cubins to valid LLVM IR.
-
KindHML: formal verification of smart contracts based on Hennessy-Milner logic
An encoding of Solidity contracts and first-order Hennessy-Milner logic into Lustre enables Kind 2 model checking of complex temporal properties in smart contracts.
-
Sparse Concept Anchoring for Interpretable and Controllable Neural Representations
Sparse Concept Anchoring biases neural latent spaces toward targeted concepts using under 0.1% labels per concept, enabling reversible steering via projection and permanent removal via weight ablation with minimal side effects on other features.
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The Procedural Semantics Gap in Structured CTI: A Measurement-Driven STIX Analysis for APT Emulation
Structured CTI standards like ATT&CK describe adversary actions but lack the ordering, preconditions, and environmental details needed for direct multi-stage emulation, and a translation method can bridge this gap when assumptions are recorded.
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Not Every Subject Should Stay: Machine Unlearning for Noisy Engagement Recognition
Approximate subject-level unlearning recovers 89.3% and 92.5% of oracle performance gains on EngageNet and DAiSEE at roughly one-quarter the retraining cost in K=3 forget-set regimes.
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Adding Compilation Metadata To Binaries To Make Disassembly Decidable
Augmenting binaries with compilation metadata makes disassembly decidable, enables correct lifting to recompilable representations, and improves analysis reliability while adding negligible size and no runtime overhead.
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How Generative AI Empowers Attackers and Defenders Across the Trust & Safety Landscape
Generative AI boosts attackers' ability to create harmful content at scale while also enabling defenders to detect threats, support users, and improve moderation processes.