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Articles tagged with: #provenance Clear filter
Secure and Robust Watermarking for AI-generated Images: A Comprehensive Survey

Secure and Robust Watermarking for AI-generated Images: A Comprehensive Survey

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2510.02384v1 Announce Type: new Abstract: The rapid advancement of generative artificial intelligence (Gen-AI) has facilitated the effortless creation of high-quality images, while simultaneously raising critical concerns regarding intellectual property protection, authenticity, and accountability. Watermarking has emerged as a promising solution to these challenges by distinguishing AI-generated images from natural content, ensuring provenance, and fostering trustworthy digital...

#ai #provenance
DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis

DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2509.26562v1 Announce Type: new Abstract: Deep neural networks (DNNs) are increasingly being deployed in high-stakes applications, from self-driving cars to biometric authentication. However, their unpredictable and unreliable behaviors in real-world settings require new approaches to characterize and ensure their reliability. This paper introduces DeepProv, a novel and customizable system designed to capture and characterize the runtime behavior of DNNs during inference by using their...

#authentication #provenance
SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From

SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2509.26404v1 Announce Type: new Abstract: Fingerprinting Large Language Models (LLMs) is essential for provenance verification and model attribution. Existing methods typically extract post-hoc signatures based on training dynamics, data exposure, or hyperparameters -- properties that only emerge after training begins. In contrast, we propose a stronger and more intrinsic notion of LLM fingerprinting: SeedPrints, a method that leverages random initialization biases as persistent,...

#ai #provenance
Fingerprinting LLMs via Prompt Injection

Fingerprinting LLMs via Prompt Injection

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2509.25448v1 Announce Type: new Abstract: Large language models (LLMs) are often modified after release through post-processing such as post-training or quantization, which makes it challenging to determine whether one model is derived from another. Existing provenance detection methods have two main limitations: (1) they embed signals into the base model before release, which is infeasible for already published models, or (2) they compare outputs across models using hand-crafted or...

#detection-engineering #provenance
An Ensemble Framework for Unbiased Language Model Watermarking

An Ensemble Framework for Unbiased Language Model Watermarking

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2509.24043v1 Announce Type: new Abstract: As large language models become increasingly capable and widely deployed, verifying the provenance of machine-generated content is critical to ensuring trust, safety, and accountability. Watermarking techniques have emerged as a promising solution by embedding imperceptible statistical signals into the generation process. Among them, unbiased watermarking is particularly attractive due to its theoretical guarantee of preserving the language...

#security-tools #provenance
Context Lineage Assurance for Non-Human Identities in Critical Multi-Agent Systems

Context Lineage Assurance for Non-Human Identities in Critical Multi-Agent Systems

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2509.18415v1 Announce Type: new Abstract: The proliferation of autonomous software agents necessitates rigorous frameworks for establishing secure and verifiable agent-to-agent (A2A) interactions, particularly when such agents are instantiated as non-human identities(NHIs). We extend the A2A paradigm [1 , 2] by introducing a cryptographically grounded mechanism for lineage verification, wherein the provenance and evolution of NHIs are anchored in append-only Merkle tree structures modeled...

#provenance
TFLAG:Towards Practical APT Detection via Deviation-Aware Learning on Temporal Provenance Graph

TFLAG:Towards Practical APT Detection via Deviation-Aware Learning on Temporal Provenance Graph

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2501.06997v2 Announce Type: replace Abstract: Advanced Persistent Threat (APT) have grown increasingly complex and concealed, posing formidable challenges to existing Intrusion Detection Systems in identifying and mitigating these attacks. Recent studies have incorporated graph learning techniques to extract detailed information from provenance graphs, enabling the detection of attacks with greater granularity. Nevertheless, existing studies have largely overlooked the continuous yet...

#detection-engineering #provenance
Google Pixel 10 Adds C2PA Support to Verify AI-Generated Media Authenticity

Google Pixel 10 Adds C2PA Support to Verify AI-Generated Media Authenticity

The Hacker News • • thehackernews.com

Google on Tuesday announced that its new Google Pixel 10 phones support the Coalition for Content Provenance and Authenticity (C2PA) standard out of the box to verify the origin and history of digital content. To that end, support for C2PA's Content Credentials has been added to Pixel Camera and Google Photos apps for Android. The move, Google said, is designed to further digital media

#provenance
Factuality Beyond Coherence: Evaluating LLM Watermarking Methods for Medical Texts

Factuality Beyond Coherence: Evaluating LLM Watermarking Methods for Medical Texts

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2509.07755v1 Announce Type: cross Abstract: As large language models (LLMs) adapted to sensitive domains such as medicine, their fluency raises safety risks, particularly regarding provenance and accountability. Watermarking embeds detectable patterns to mitigate these risks, yet its reliability in medical contexts remains untested. Existing benchmarks focus on detection-quality tradeoffs, overlooking factual risks under low-entropy settings often exploited by watermarking's reweighting...

#detection-engineering #ai #provenance
KnowHow: Automatically Applying High-Level CTI Knowledge for Interpretable and Accurate Provenance Analysis

KnowHow: Automatically Applying High-Level CTI Knowledge for Interpretable and Accurate Provenance Analysis

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2509.05698v1 Announce Type: new Abstract: High-level natural language knowledge in CTI reports, such as the ATT&CK framework, is beneficial to counter APT attacks. However, how to automatically apply the high-level knowledge in CTI reports in realistic attack detection systems, such as provenance analysis systems, is still an open problem. The challenge stems from the semantic gap between the knowledge and the low-level security logs: while the knowledge in CTI reports is written in...

#security-tools #incident #detection-engineering #mitre-attack #provenance
EverTracer: Hunting Stolen Large Language Models via Stealthy and Robust Probabilistic Fingerprint

EverTracer: Hunting Stolen Large Language Models via Stealthy and Robust Probabilistic Fingerprint

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2509.03058v1 Announce Type: new Abstract: The proliferation of large language models (LLMs) has intensified concerns over model theft and license violations, necessitating robust and stealthy ownership verification. Existing fingerprinting methods either require impractical white-box access or introduce detectable statistical anomalies. We propose EverTracer, a novel gray-box fingerprinting framework that ensures stealthy and robust model provenance tracing. EverTracer is the first to...

#security-tools #provenance
LLM-driven Provenance Forensics for Threat Investigation and Detection

LLM-driven Provenance Forensics for Threat Investigation and Detection

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2508.21323v1 Announce Type: new Abstract: We introduce PROVSEEK, an LLM-powered agentic framework for automated provenance-driven forensic analysis and threat intelligence extraction. PROVSEEK employs specialized toolchains to dynamically retrieve relevant context by generating precise, context-aware queries that fuse a vectorized threat report knowledge base with data from system provenance databases. The framework resolves provenance queries, orchestrates multiple role-specific agents...

#security-tools #detection-engineering #ai #provenance
Robustness Assessment and Enhancement of Text Watermarking for Google's SynthID

Robustness Assessment and Enhancement of Text Watermarking for Google's SynthID

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2508.20228v1 Announce Type: new Abstract: Recent advances in LLM watermarking methods such as SynthID-Text by Google DeepMind offer promising solutions for tracing the provenance of AI-generated text. However, our robustness assessment reveals that SynthID-Text is vulnerable to meaning-preserving attacks, such as paraphrasing, copy-paste modifications, and back-translation, which can significantly degrade watermark detectability. To address these limitations, we propose SynGuard, a hybrid...

#ai #provenance
DSLRoot, Proxies, and the Threat of 'Legal Botnets'

DSLRoot, Proxies, and the Threat of 'Legal Botnets'

Krebs on Security • • krebsonsecurity.com

The cybersecurity community on Reddit responded in disbelief this month when a self-described Air National Guard member with top secret security clearance began questioning the arrangement they'd made with company called DSLRoot, which was paying $250 a month to plug a pair of laptops into the Redditor's high-speed Internet connection in the United States. This post examines the history and provenance of DSLRoot, one of the oldest "residential proxy" networks with origins in Russia and Eastern...

#provenance
Can AI Keep a Secret? Contextual Integrity Verification: A Provable Security Architecture for LLMs

Can AI Keep a Secret? Contextual Integrity Verification: A Provable Security Architecture for LLMs

cs.CR updates on arXiv.org • • arxiv.org

arXiv:2508.09288v2 Announce Type: replace Abstract: Large language models (LLMs) remain acutely vulnerable to prompt injection and related jailbreak attacks; heuristic guardrails (rules, filters, LLM judges) are routinely bypassed. We present Contextual Integrity Verification (CIV), an inference-time security architecture that attaches cryptographically signed provenance labels to every token and enforces a source-trust lattice inside the transformer via a pre-softmax hard attention mask (with...

#ai #provenance