arXiv subjects

Peiran Wang

Publications and source records attributed to Peiran Wang.

At least 19 recordsLinked to original sources

arXiv2026

VIGIL: Runtime Enforcement of Behavioral Specifications in AI Agent Skills

Agentic systems increasingly act through third-party skills, allowing model-generated decisions to affect files, communication channels, and cyber-physical devices. These skills often include natural-language specifications that define access permissions, disclosure limits, execution privileges, and required preconditions. Although such specifications describe the intended boundaries of skill behavior, they do not by themselves provide executable runtime enforcement. Enforcing them raises a contextual granularity challenge: even when a policy is written for a particular task context, a monitor must still decide which events to observe, what state to retain, how far across the execution to reason, and where to intervene. Choosing the wrong granularity can either block benign executions or miss violations that emerge only across multiple actions. Most existing enforcement mechanisms, however, assume a fixed event model or enforcement point. In this work, we present VIGIL, an end-to-end runtime enforcement framework for agentic systems. VIGIL checks an agent's actual execution trace against behavioral policies from skill specifications, operator-defined constraints, and global rules spanning multiple skills. To make such policies executable, VIGIL introduces a policy language that captures context-specific enforcement requirements over agent-tool events, including temporal dependencies, argument constraints, and value-flow conditions. The language is paired with symbolic evaluation rules that translate policies into SMT constraints over finite traces, allowing VIGIL to detect violations that depend on event order, argument relationships, or cross-call value flow rather than relying on fixed single-call filters. On real LLM-agent runs spanning office-document, operational, and engineering tasks, VIGIL detects policy violations with over 95% recall and a false-positive rate below 10%.

cs.CR↗

arXiv2026

Aligning Provenance with Authorization: A Dual-Graph Defense for LLM Agents

LLM-based agents are increasingly deployed in high-stakes scenarios such as email management, financial transactions, and code execution, where they interact with the external world through tool calling. During execution, these agents must read external data sources (emails, webpages, files) that attackers can control; through indirect prompt injection, attackers embed malicious instructions in this data to manipulate agents into performing unauthorized operations such as transferring funds to attacker-controlled accounts. Existing defenses either perform tool-call-level value checking without tracking where parameter values originate, or analyze execution traces from a single perspective without a clean authorization baseline for comparison. We propose AuthGraph, a dual-graph alignment defense framework that constructs two complementary graphs: an injected reasoning graph that models information provenance from the actual execution trajectory (including potentially manipulated attributions), and an authorization graph derived from the user's intent in an isolated clean context that is information-theoretically impossible to be influenced by injection; a graph alignment checker then structurally compares the two graphs to detect both tool-level and parameter-source-level deviations. On AgentDojo, AuthGraph reduces the attack success rate from 40% to 1% while maintaining 76% task completion rate on GPT-4o; on AgentDyn, it reduces the attack success rate from 39% to 2% while preserving 51% utility, outperforming state-of-the-art defenses including CaMeL, DRIFT, and Progent. To our knowledge, AuthGraph is the first agent security defense to structurally compare authorization specifications against execution provenance at the parameter-source level, achieving fine-grained injection detection without sacrificing agent flexibility.

cs.CR↗

arXiv2026

Reframing LLM Agent Security as an Agent-Human Interaction Problem

We argue that LLM agent security is fundamentally an agent-human interaction (AHI) problem, not a purely algorithmic one. To substantiate this position, we conduct a systematic analysis of 59 academic papers, 21 production agent systems, and 26 security plugins as of April 2026. Our analysis reveals a striking pattern: the three widely deployed human-centric security mechanisms (policy specification, runtime approval, and scope configuration) dominate industry practice, each adopted by at least 14 of 21 systems (14, 15, and 16, respectively), while the categories most heavily studied in academia (intent anchoring and trust labeling) see zero production deployment. Yet current human participation mechanisms are far from satisfactory: they suffer from a fundamental trade-off between cognitive burden and security guarantees, leaving users caught between approval fatigue and uncontrolled agent autonomy. We make three contributions. First, through a systematic comparison of LLM-based and human-based intent alignment, we argue that human participation in agent security decisions is indispensable given current capabilities. Second, we quantify a pronounced industry-academia mismatch: the security mechanisms that practitioners actually deploy receive scant research attention, while the approaches that researchers favor remain undeployed. Third, we propose a three-direction research agenda and call for AHI security to be recognized as a first-class research citizen, one that demands its own design principles, evaluation methods, and theoretical foundations.

cs.CR↗

arXiv2026

Securing LLM Agents Need Intent-to-Execution Integrity

This position paper argues that securing LLM agents requires first defining an end-to-end correctness property that specifies when an agent's execution faithfully reflects the user's intent. Modern LLM agents operate over an \emph{intent-to-execution pipeline}, where natural-language instructions are translated into concrete system operations such as tool calls, API requests, and code execution. While recent defenses have made progress in constraining how agents construct tool calls, most existing formulations implicitly assume that tools are trusted. The emergence of systems such as OpenClaw, with open ecosystems of third-party skills and direct access to user environments, breaks this assumption and exposes new failure modes, including malicious or over-privileged components in the execution pipeline. Despite rapid progress in defense mechanisms, there is no adequate correctness property that defines what ``secure'' means for LLM agents, nor a principled way to evaluate the coverage of existing defenses. We observe that LLM agents are structurally analogous to compilers, where security violations correspond to mis-executions that do not preserve user intent. Drawing on this analogy, we identify two fundamental problem sources -- untrusted data ingestion and untrusted tool execution -- and derive four integrity properties that must hold simultaneously: \emph{Tool Integrity}, \emph{Instruction Integrity}, \emph{Judgment Integrity}, and \emph{Data Flow Integrity}. We call their conjunction \emph{intent-to-execution integrity}. Analyzing existing agentic defenses against these properties reveals that current systems provide only partial and non-compositional coverage, leaving fundamental gaps in securing modern LLM agents.

cs.CR↗

arXiv2026

Options, Not Clicks: Lattice Refinement for Consent-Driven MCP Authorization

As Model Context Protocol adoption grows, securing tool invocations via meaningful user consent has become a critical challenge, as existing methods, broad always allow toggles or opaque LLM-based decisions, fail to account for dangerous call arguments and often lead to consent fatigue. In this work, we present Conleash, a client-side middleware that enforces boundary-scoped authorization by utilizing a risk lattice to auto-permit safe calls within known boundaries while escalating risks, a policy engine for user-defined invariants, and a refinement loop that converts user decisions into reusable rules. Evaluated on 984 real-world traces, Conleash achieved 98.2% accuracy, caught 99.4% of escalations, and added only 8.2 ms of overhead for policy verification; furthermore, in a user study where N=16, participants significantly preferred Conleash scoped permissions over traditional methods, citing higher trust and reduced prompting.

cs.CR↗

arXiv2026

FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models

Large language models (LLMs) often exhibit undesirable behaviors, such as safety violations and hallucinations. Although inference-time steering offers a cost-effective way to adjust model behavior without updating its parameters, existing methods often fail to be simultaneously effective, utility-preserving, and training-efficient due to their rigid, one-size-fits-all designs and limited adaptability. In this work, we present FineSteer, a novel steering framework that decomposes inference-time steering into two complementary stages: conditional steering and fine-grained vector synthesis, allowing fine-grained control over when and how to steer internal representations. In the first stage, we introduce a Subspace-guided Conditional Steering (SCS) mechanism that preserves model utility by avoiding unnecessary steering. In the second stage, we propose a Mixture-of-Steering-Experts (MoSE) mechanism that captures the multimodal nature of desired steering behaviors and generates query-specific steering vectors for improved effectiveness. Through tailored designs in both SCS and MoSE, FineSteer maintains robust performance on general queries while adaptively optimizing steering vectors for targeted inputs in a training-efficient manner. Extensive experiments on safety and truthfulness benchmarks show that FineSteer outperforms state-of-the-art methods in overall performance, achieving stronger steering performance with minimal utility loss. Code is available at https://github.com/YukinoAsuna/FineSteer

cs.LG↗

arXiv2026

From Docs to Descriptions: Smell-Aware Evaluation of MCP Server Descriptions

The Model Context Protocol (MCP) has rapidly become a de facto standard for connecting LLM-based agents with external tools via reusable MCP servers. In practice, however, server selection and onboarding rely heavily on free-text tool descriptions that are intentionally loosely constrained. Although this flexibility largely ensures the scalability of MCP servers, it also creates a reliability gap that descriptions often misrepresent or omit key semantics, increasing trial-and-error integration, degrading agent behavior, and potentially introducing security risks. To this end, we present the first systematic study of description smells in MCP tool descriptions and their impact on usability. Specifically, we synthesize software/API documentation practices and agentic tool-use requirements into a four-dimensional quality standard: accuracy, functionality, information completeness, and conciseness, covering 18 specific smell categories. Using this standard, we conducted a large-scale empirical study on a well-constructed dataset of 10,831 MCP servers. We find that description smells are pervasive (e.g., 73% repeated tool names, thousands with incorrect parameter semantics or missing return descriptions), reflecting a "code-first, description-last" pattern. Through a controlled mutation-based study, we show these smells significantly affect LLM tool selection, with functionality and accuracy having the largest effects (+11.6% and +8.8%, p < 0.001). In competitive settings with functionally equivalent servers, standard-compliant descriptions reach 72% selection probability (260% over a 20% baseline), demonstrating that smell-guided remediation yields substantial practical benefits. We release our labeled dataset and standards to support future work on reliable and secure MCP ecosystems.

cs.SE↗

arXiv2026

The Landscape of Prompt Injection Threats in LLM Agents: From Taxonomy to Analysis

The evolution of Large Language Models (LLMs) has resulted in a paradigm shift towards autonomous agents, necessitating robust security against Prompt Injection (PI) vulnerabilities where untrusted inputs hijack agent behaviors. This SoK presents a comprehensive overview of the PI landscape, covering attacks, defenses, and their evaluation practices. Through a systematic literature review and quantitative analysis, we establish taxonomies that categorize PI attacks by payload generation strategies (heuristic vs. optimization) and defenses by intervention stages (text, model, and execution levels). Our analysis reveals a key limitation shared by many existing defenses and benchmarks: they largely overlook context-dependent tasks, in which agents are authorized to rely on runtime environmental observations to determine actions. To address this gap, we introduce AgentPI, a new benchmark designed to systematically evaluate agent behavior under context-dependent interaction settings. Using AgentPI, we empirically evaluate representative defenses and show that no single approach can simultaneously achieve high trustworthiness, high utility, and low latency. Moreover, we show that many defenses appear effective under existing benchmarks by suppressing contextual inputs, yet fail to generalize to realistic agent settings where context-dependent reasoning is essential. This SoK distills key takeaways and open research problems, offering structured guidance for future research and practical deployment of secure LLM agents.

cs.CR↗

arXiv2025

Large Language Model-based Data Science Agent: A Survey

The rapid advancement of Large Language Models (LLMs) has driven novel applications across diverse domains, with LLM-based agents emerging as a crucial area of exploration. This survey presents a comprehensive analysis of LLM-based agents designed for data science tasks, summarizing insights from recent studies. From the agent perspective, we discuss the key design principles, covering agent roles, execution, knowledge, and reflection methods. From the data science perspective, we identify key processes for LLM-based agents, including data preprocessing, model development, evaluation, visualization, etc. Our work offers two key contributions: (1) a comprehensive review of recent developments in applying LLMbased agents to data science tasks; (2) a dual-perspective framework that connects general agent design principles with the practical workflows in data science.

cs.AI↗

arXiv2025

AgentArmor: Enforcing Program Analysis on Agent Runtime Trace to Defend Against Prompt Injection

Large Language Model (LLM) agents offer a powerful new paradigm for solving various problems by combining natural language reasoning with the execution of external tools. However, their dynamic and non-transparent behavior introduces critical security risks, particularly in the presence of prompt injection attacks. In this work, we propose a novel insight that treats the agent runtime traces as structured programs with analyzable semantics. Thus, we present AgentArmor, a program analysis framework that converts agent traces into graph intermediate representation-based structured program dependency representations (e.g., CFG, DFG, and PDG) and enforces security policies via a type system. AgentArmor consists of three key components: (1) a graph constructor that reconstructs the agent's runtime traces as graph-based intermediate representations with control and data flow described within; (2) a property registry that attaches security-relevant metadata of interacted tools \& data, and (3) a type system that performs static inference and checking over the intermediate representation. By representing agent behavior as structured programs, AgentArmor enables program analysis for sensitive data flow, trust boundaries, and policy violations. We evaluate AgentArmor on the AgentDojo benchmark, the results show that AgentArmor can reduce the ASR to 3\%, with the utility drop only 1\%.

cs.CR↗

arXiv2025

Dataset Distillation via the Wasserstein Metric

Dataset Distillation (DD) aims to generate a compact synthetic dataset that enables models to achieve performance comparable to training on the full large dataset, significantly reducing computational costs. Drawing from optimal transport theory, we introduce WMDD (Wasserstein Metric-based Dataset Distillation), a straightforward yet powerful method that employs the Wasserstein metric to enhance distribution matching. We compute the Wasserstein barycenter of features from a pretrained classifier to capture essential characteristics of the original data distribution. By optimizing synthetic data to align with this barycenter in feature space and leveraging per-class BatchNorm statistics to preserve intra-class variations, WMDD maintains the efficiency of distribution matching approaches while achieving state-of-the-art results across various high-resolution datasets. Our extensive experiments demonstrate WMDD's effectiveness and adaptability, highlighting its potential for advancing machine learning applications at scale.

cs.CV↗

arXiv2025

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models

Large foundation models (LFMs) are susceptible to two distinct vulnerabilities: hallucinations and jailbreak attacks. While typically studied in isolation, we observe that defenses targeting one often affect the other, hinting at a deeper connection. We propose a unified theoretical framework that models jailbreaks as token-level optimization and hallucinations as attention-level optimization. Within this framework, we establish two key propositions: (1) \textit{Similar Loss Convergence} - the loss functions for both vulnerabilities converge similarly when optimizing for target-specific outputs; and (2) \textit{Gradient Consistency in Attention Redistribution} - both exhibit consistent gradient behavior driven by shared attention dynamics. We validate these propositions empirically on LLaVA-1.5 and MiniGPT-4, showing consistent optimization trends and aligned gradients. Leveraging this connection, we demonstrate that mitigation techniques for hallucinations can reduce jailbreak success rates, and vice versa. Our findings reveal a shared failure mode in LFMs and suggest that robustness strategies should jointly address both vulnerabilities.

cs.CV↗

arXiv2025

Towards Adversarially Robust Dataset Distillation by Curvature Regularization

Dataset distillation (DD) allows datasets to be distilled to fractions of their original size while preserving the rich distributional information, so that models trained on the distilled datasets can achieve a comparable accuracy while saving significant computational loads. Recent research in this area has been focusing on improving the accuracy of models trained on distilled datasets. In this paper, we aim to explore a new perspective of DD. We study how to embed adversarial robustness in distilled datasets, so that models trained on these datasets maintain the high accuracy and meanwhile acquire better adversarial robustness. We propose a new method that achieves this goal by incorporating curvature regularization into the distillation process with much less computational overhead than standard adversarial training. Extensive empirical experiments suggest that our method not only outperforms standard adversarial training on both accuracy and robustness with less computation overhead but is also capable of generating robust distilled datasets that can withstand various adversarial attacks. Our implementation is available at: https://github.com/yumozi/GUARD.

cs.LG↗

arXiv2025

Astra: Efficient and Money-saving Automatic Parallel Strategies Search on Heterogeneous GPUs

In this paper, we introduce an efficient and money-saving automatic parallel strategies search framework on heterogeneous GPUs: Astra. First, Astra searches for the efficiency-optimal parallel strategy in both GPU configurations search space (GPU types and GPU numbers) and parallel parameters search space. Then, Astra also provides the solution on heterogeneous GPUs by mathematically modeling the time consumption of heterogeneous training. At last, Astra is the first to propose the automatic parallel strategy search on money-saving. The experiment results demonstrate that Astra can achieve better throughput than expert-designed strategies. The search time cost for Astra can also be limited to 1.27 seconds in a single-GPU setting and less than 1.35 minutes in a heterogeneous-GPU setting on average with an accuracy of over 95%.

cs.DC↗

arXiv2025

What are Models Thinking about? Understanding Large Language Model Hallucinations "Psychology" through Model Inner State Analysis

Large language model (LLM) systems suffer from the models' unstable ability to generate valid and factual content, resulting in hallucination generation. Current hallucination detection methods heavily rely on out-of-model information sources, such as RAG to assist the detection, thus bringing heavy additional latency. Recently, internal states of LLMs' inference have been widely used in numerous research works, such as prompt injection detection, etc. Considering the interpretability of LLM internal states and the fact that they do not require external information sources, we introduce such states into LLM hallucination detection. In this paper, we systematically analyze different internal states' revealing features during inference forward and comprehensively evaluate their ability in hallucination detection. Specifically, we cut the forward process of a large language model into three stages: understanding, query, generation, and extracting the internal state from these stages. By analyzing these states, we provide a deep understanding of why the hallucinated content is generated and what happened in the internal state of the models. Then, we introduce these internal states into hallucination detection and conduct comprehensive experiments to discuss the advantages and limitations.

cs.CL↗

arXiv2025

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models

Fairness is a fundamental principle in medical ethics. Vision Language Models (VLMs) have shown significant potential in the medical field due to their ability to leverage both visual and linguistic contexts, reducing the need for large datasets and enabling the performance of complex tasks. However, the exploration of fairness within VLM applications remains limited. Applying VLMs without a comprehensive analysis of fairness could lead to concerns about equal treatment opportunities and diminish public trust in medical deep learning models. To build trust in medical VLMs, we propose Fair-MoE, a model specifically designed to ensure both fairness and effectiveness. Fair-MoE comprises two key components: \textit{the Fairness-Oriented Mixture of Experts (FO-MoE)} and \textit{the Fairness-Oriented Loss (FOL)}. FO-MoE is designed to leverage the expertise of various specialists to filter out biased patch embeddings and use an ensemble approach to extract more equitable information relevant to specific tasks. FOL is a novel fairness-oriented loss function that not only minimizes the distances between different attributes but also optimizes the differences in the dispersion of various attributes' distributions. Extended experiments demonstrate the effectiveness and fairness of Fair-MoE. Tested on the Harvard-FairVLMed dataset, Fair-MoE showed improvements in both fairness and accuracy across all four attributes. Code will be publicly available.

cs.CV↗

arXiv2024

RePD: Defending Jailbreak Attack through a Retrieval-based Prompt Decomposition Process

In this study, we introduce RePD, an innovative attack Retrieval-based Prompt Decomposition framework designed to mitigate the risk of jailbreak attacks on large language models (LLMs). Despite rigorous pretraining and finetuning focused on ethical alignment, LLMs are still susceptible to jailbreak exploits. RePD operates on a one-shot learning model, wherein it accesses a database of pre-collected jailbreak prompt templates to identify and decompose harmful inquiries embedded within user prompts. This process involves integrating the decomposition of the jailbreak prompt into the user's original query into a one-shot learning example to effectively teach the LLM to discern and separate malicious components. Consequently, the LLM is equipped to first neutralize any potentially harmful elements before addressing the user's prompt in a manner that aligns with its ethical guidelines. RePD is versatile and compatible with a variety of open-source LLMs acting as agents. Through comprehensive experimentation with both harmful and benign prompts, we have demonstrated the efficacy of our proposed RePD in enhancing the resilience of LLMs against jailbreak attacks, without compromising their performance in responding to typical user requests.

cs.CR↗

arXiv2024

DistDD: Distributed Data Distillation Aggregation through Gradient Matching

In this paper, we introduce DistDD, a novel approach within the federated learning framework that reduces the need for repetitive communication by distilling data directly on clients' devices. Unlike traditional federated learning that requires iterative model updates across nodes, DistDD facilitates a one-time distillation process that extracts a global distilled dataset, maintaining the privacy standards of federated learning while significantly cutting down communication costs. By leveraging the DistDD's distilled dataset, the developers of the FL can achieve just-in-time parameter tuning and neural architecture search over FL without repeating the whole FL process multiple times. We provide a detailed convergence proof of the DistDD algorithm, reinforcing its mathematical stability and reliability for practical applications. Our experiments demonstrate the effectiveness and robustness of DistDD, particularly in non-i.i.d. and mislabeled data scenarios, showcasing its potential to handle complex real-world data challenges distinctively from conventional federated learning methods. We also evaluate DistDD's application in the use case and prove its effectiveness and communication-savings in the NAS use case.

cs.LG↗