Prismata cuts web-agent prompt-injection attack success from 85.5% to 0.7% via Biba-inspired DOM trust labeling and mechanical least-privilege confinement without site annotations.
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6 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
6
Pith papers citing it
3
external citations · external index
years
2026 6representative citing papers
Data Flow Control formalizes data safety as aggregate predicates over provenance monomials and implements enforcement via the Passant query rewriting layer achieving near-zero overhead across five DBMS engines.
PopPy combines an ahead-of-time compiler and runtime to extract parallelism from Python compound AI applications, delivering up to 6.4x end-to-end speedups while preserving sequential semantics.
Presents a distributionally robust optimization method for sound probabilistic verification of Datalog policies in AI agents that bounds violation risk regardless of predicate correlations.
Introduces a benchmarking suite for compound AI applications to support cross-stack performance, cost, and resource analysis for hardware-software co-design.
ClayBuddy adds agent-editable context, extended prompts, a command classifier, and deterministic guardrails to coding agent harnesses and shows statistically significant safety gains across 8 evaluations.
citing papers explorer
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Prismata: Confining Cross-Site Prompt Injection in Web Agents
Prismata cuts web-agent prompt-injection attack success from 85.5% to 0.7% via Biba-inspired DOM trust labeling and mechanical least-privilege confinement without site annotations.
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Data Flow Control: Data Safety Policies for AI Agents
Data Flow Control formalizes data safety as aggregate predicates over provenance monomials and implements enforcement via the Passant query rewriting layer achieving near-zero overhead across five DBMS engines.
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PopPy: Opportunistically Exploiting Parallelism in Python Compound AI Applications
PopPy combines an ahead-of-time compiler and runtime to extract parallelism from Python compound AI applications, delivering up to 6.4x end-to-end speedups while preserving sequential semantics.
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Efficient and Sound Probabilistic Verification for AI Agents
Presents a distributionally robust optimization method for sound probabilistic verification of Datalog policies in AI agents that bounds violation risk regardless of predicate correlations.
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Benchmarking Compound AI Applications for Hardware-Software Co-Design
Introduces a benchmarking suite for compound AI applications to support cross-stack performance, cost, and resource analysis for hardware-software co-design.
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ClayBuddy: A Framework, Evaluation, & Mitigation of Coding Agent Failures
ClayBuddy adds agent-editable context, extended prompts, a command classifier, and deterministic guardrails to coding agent harnesses and shows statistically significant safety gains across 8 evaluations.