Source author record

Qinfeng Li

Explore this author’s publications, collaborators and research topics. This catalog record has not been claimed by its author.

ResearcherUnclaimed source record

math.AP Artificial Intelligence Cryptography and Security Machine Learning math.FA

Catalog footprint

Research overview

6works

5topics

4close collaborators

Actions

Is this your work?

Open graph Browse papers

preprint2026arXiv

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts

LLM agents rely on prompts to implement task-specific capabilities based on foundation LLMs, making agent prompts valuable intellectual property. However, in untrusted deployments, adversaries can copy and reuse these prompts with other proprietary LLMs, causing economic losses. To protect these prompts, we identify four key challenges: proactivity, runtime protection, usability, and non-portability that existing approaches fail to address. We present PragLocker, a prompt protection scheme that satisfies these requirements. PragLocker constructs function-preserving obfuscated prompts by anchoring semantics with code symbols and then using target-model feedback to inject noise, yielding prompts that only work on the target LLM. Experiments across multiple agent systems, datasets, and foundation LLMs show that PragLocker substantially reduces cross-LLM portability, maintains target performance, and remains robust against adaptive attackers.

preprint2026arXiv

Towards Steering without Sacrifice: Principled Training of Steering Vectors for Prompt-only Interventions

Recently, steering vectors (SVs) have emerged as an effective and lightweight approach to steer behaviors of large language models (LLMs), among which fine-tuned SVs are more effective than optimization-free ones. However, current approaches to fine-tuned SVs suffer from two limitations. First, they require careful selection of steering factors on a per-SV basis to balance steering effectiveness and generation quality at inference time. Second, they operate as full-sequence SVs (FSSVs), which can sacrifice generation quality regardless of factor selection due to excessive intervention on the model generation process. To address the first limitation, we propose joint training of steering factors and directions, such that post-hoc factor selection is no longer required. Using neural network scaling theory, we find that moderately large initialization sizes and learning rates for steering factors are essential for stability and efficiency of joint training. To tackle the second limitation, we draw inspiration from representation fine-tuning and introduce Prompt-only SV (PrOSV), an SV that intervenes only on a few prompt tokens. Our empirical results show that PrOSV outperforms traditional FSSVs on AxBench when using our joint training scheme. We also find that PrOSV achieves a better tradeoff between general model utility and adversarial robustness than FSSV.

preprint2022arXiv

Some geometric inequalities related to Liouville equation

In this paper, we prove that if $u$ is a solution to the Liouville equation \begin{align} \label{scalliouville} Δu+e^{2u} =0 \quad \mbox{in $\mathbb{R}^2$ ,} \end{align}then the diameter of $\mathbb{R}^2$ under the conformal metric $g=e^{2u}δ$ is bounded below by $π$ . Here $δ$ is the Euclidean metric in $\mathbb{R}^2$ . Moreover, we explicitly construct a family of solutions such that the corresponding diameters of $\mathbb{R}^2$ range over $[π,2π)$ . We also discuss supersolutions. We show that if $u$ is a supersolution and $\int_{\mathbb{R}^2} e^{2u} dx<\infty$ , then the diameter of $\mathbb{R}^2$ under the metric $e^{2u}δ$ is less than or equal to $2π$ . For radial supersolutions, we use both analytical and geometric approaches to prove some inequalities involving conformal lengths and areas of disks in $\mathbb{R}^2$ . We also discuss the connection of the above results with the sphere covering inequality in the case of Gaussian curvature bounded below by $1$ . Higher dimensional generalizations are also discussed.

preprint2022arXiv

Stability analysis on the thermal insulation problems

Based on the domain variational point of view, we carry on stability analysis on two shape optimization problems from thermal insulation background. The novelty is that, we do not require that the second variation is normal to the boundary. For example, translation variation is not normal, but as one can see in our work, it not only plays a role in obtaining the necessary and sufficient condition for stability of ball shape in the first problem when heat source is radial, but also is essential in deriving the precise value of symmetry breaking threshold of insulation material in the second problem, which turns out to be related to isoperimetric constant and in turn implies that ball shapes are stable in two dimensions.

preprint2020arXiv

Compactness of $M$ -uniform domains and optimal thermal insulation problems

In this paper, we will consider an optimal shape problem of heat insulation introduced by Bucur-Buttazzo-Nitsch. We will establish the existence of optimal shapes in the class of $M$ -uniform domains. We will also show that balls are stable solutions of the optimal heat insulation problem.

preprint2020arXiv

Traces and Extensions of Bounded Divergence-Measure Fields on Rough Open Sets

We prove that an open set $Ω\subset \mathbb{R}^n$ can be approximated by smooth sets of uniformly bounded perimeter from the interior if and only if the open set $Ω$ satisfies \begin{align*} &\qquad \qquad\qquad\qquad\qquad\qquad\qquad \mathscr{H}^{n-1}(\partial Ω\setminus Ω^0)<\infty, \qquad &&\quad\qquad\qquad \qquad\qquad (*) \end{align*} where $Ω^0$ is the measure-theoretic exterior of $Ω$ . Furthermore, we show that condition (*) implies that the open set $Ω$ is an extension domain for bounded divergence-measure fields, which improves the previous results that require a strong condition that $\mathscr{H}^{n-1}(\partial Ω)<\infty$ . As an application, we establish a Gauss-Green formula up to the boundary on any open set $Ω$ satisfying condition (*) for bounded divergence-measure fields, for which the corresponding normal trace is shown to be a bounded function concentrated on $\partial Ω\setminus Ω^0$ . This new formula does not require the set of integration to be compactly contained in the domain where the vector field is defined. In addition, we also analyze the solvability of the divergence equation on a rough domain with prescribed trace on the boundary, as well as the extension domains for bounded $BV$ functions.

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Qinfeng Li

Research overview

Is this your work?

6 publications

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts

Towards Steering without Sacrifice: Principled Training of Steering Vectors for Prompt-only Interventions

Some geometric inequalities related to Liouville equation

Stability analysis on the thermal insulation problems

Compactness of $M$ -uniform domains and optimal thermal insulation problems

Traces and Extensions of Bounded Divergence-Measure Fields on Rough Open Sets

See the researcher in context

Building this map preview

Qinfeng Li

Research overview

Is this your work?

6 publications

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts

Towards Steering without Sacrifice: Principled Training of Steering Vectors for Prompt-only Interventions

Some geometric inequalities related to Liouville equation

Stability analysis on the thermal insulation problems

Compactness of MMM-uniform domains and optimal thermal insulation problems

Traces and Extensions of Bounded Divergence-Measure Fields on Rough Open Sets

See the researcher in context

Building this map preview

Compactness of $M$ -uniform domains and optimal thermal insulation problems