#provenance-tracking News & Analysis

7 articles tagged with #provenance-tracking. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

7 articles

AIBearisharXiv – CS AI · Jun 97/10

🧠

Context-Fractured Decomposition Attacks on Tool-Using LLM Agents: Exploiting Artifact Provenance Gaps

Researchers demonstrate Context-Fractured Decomposition (CFD), a new class of jailbreak attacks against tool-using LLM agents that exploit gaps in artifact provenance tracking across multiple steps and system boundaries. By decomposing harmful requests across time and contexts while maintaining benign-looking intermediate artifacts, CFD achieves up to 28.3% higher success rates than existing attack methods, revealing fundamental vulnerabilities in how AI agents enforce safety guardrails in fragmented deployment environments.

AINeutralarXiv – CS AI · Apr 77/10

🧠

Causality Laundering: Denial-Feedback Leakage in Tool-Calling LLM Agents

Researchers have identified a new security vulnerability called 'causality laundering' in AI tool-calling systems, where attackers can extract private information by learning from system denials and using that knowledge in subsequent tool calls. They developed the Agentic Reference Monitor (ARM) system to detect and prevent these attacks through enhanced provenance tracking.

AINeutralarXiv – CS AI · Jun 106/10

🧠

Provenance Tracking in AI Compilers through the Lens of Coalgebra

Researchers present a coalgebra-based approach to tracking tensor and operator provenance through AI compiler transformations, addressing the challenge of maintaining computational lineage during aggressive graph rewrites. The method uses observational semantics rather than identifier propagation, with a prototype implementation called COVAN demonstrating practical viability with minimal engineering overhead.

AINeutralarXiv – CS AI · May 286/10

🧠

Efficient and Scalable Provenance Tracking for LLM-Generated Code Snippets

Researchers introduce SourceTracker, a 300M-parameter encoder combined with a hybrid two-stage pipeline that uses vector search and fingerprinting to efficiently track code provenance in LLM-generated snippets. The system achieves logarithmic-time query complexity while maintaining high precision on billion-scale datasets, addressing scalability challenges in detecting plagiarism and license violations in AI-generated code.

AINeutralarXiv – CS AI · May 96/10

🧠

DataDignity: Training Data Attribution for Large Language Models

Researchers introduce DataDignity, a new framework for attributing large language model outputs to specific training documents. The study presents FakeWiki, a benchmark of 3,537 fabricated Wikipedia articles designed to test provenance tracking, and proposes ScoringModel, a supervised contrastive ranker that improves document attribution accuracy from 35% to 52.2% recall compared to existing baselines.

AINeutralarXiv – CS AI · May 16/10

🧠

Chronology of Multi-Agent Interactions for Provenance of Evolving Information

Researchers propose a novel system for tracking provenance in multi-agent AI systems by creating chronological records of contributions during content generation. The approach uses 'symbolic chronicles'—timestamped records similar to forensic chain-of-custody documentation—enabling attribution without relying on internal memory or external metadata, addressing accountability challenges in collaborative AI.

AINeutralarXiv – CS AI · Apr 146/10

🧠

Inspectable AI for Science: A Research Object Approach to Generative AI Governance

Researchers propose AI as a Research Object (AI-RO), a governance framework that treats generative AI interactions as inspectable, documented components of scientific research rather than debating authorship. The framework combines interaction logs, metadata packaging, and provenance records to ensure accountability, particularly for security and privacy research where confidentiality and auditability are critical.

🏢 Meta