Paladin: A Policy Framework for Securing Cloud APIs by Combining Application Context with Generative AI
- URL: http://arxiv.org/abs/2603.10228v1
- Date: Tue, 10 Mar 2026 20:55:48 GMT
- Title: Paladin: A Policy Framework for Securing Cloud APIs by Combining Application Context with Generative AI
- Abstract summary: This work focus on threats that exploit application layer vulnerabilities of cloud workloads.<n>Prevention and mitigation measures against such threats need to be cognizant of application semantics.<n>We design and implement a security framework that allow cloud workload administrators to easily define and enforce policies.
- Score: 0.16469845434482486
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Enterprises and organizations today increasingly deploy in-house, cloud based applications and APIs for internal operations or external customers. These deployments deal with increasing number of threats, despite security features offered by cloud service providers. This work focus on threats that exploit application layer vulnerabilities of cloud workloads. Prevention and mitigation measures against such threats need to be cognizant of application semantics, posing a hurdle to existing solutions. In this work, we design and implement a security framework that allow cloud workload administrators to easily define and enforce policies capable of preventing (i) unrestricted resource consumption, (ii) unrestricted access to sensitive business flows, and (iii) broken authentication. Our framework, Paladin, leverages large language models to extract sufficient semantic meaning from API requests to provide cloud administrators with an application agnostic policy definition interface. Once defined, requests are automatically matched with relevant policies and enforced by high performance proxies. Evaluations with our prototype show that such a framework has broad applicability across applications, good policy identification accuracy, and reasonable overheads, making it substantially easier to define and enforce cross application policies.
Related papers
- Overprivilege Analysis of Security Policies in Serverless Cloud Applications [1.3068208613904335]
We analyze a curated dataset of 689 AWS Lambda applications comprised of 1,293 functions.<n>47.7% of applications carry excess permissions with a significant privilege reduction potential of 99.65%.<n>18.8% of applications hold unnecessary Privilege Escalation capabilities, and 12 applications had Defense Evasion permissions they did not need.
arXiv Detail & Related papers (2026-07-03T02:22:14Z) - Lockbox -- A Zero Trust Architecture for Secure Processing of Sensitive Cloud Workloads [0.1633272850273525]
This paper presents Lockbox; a Zero Trust architecture designed for secure processing of sensitive cloud workloads.<n>Lockbox applies explicit trust verification, strong isolation, least-privilege access, and policy-driven enforcement throughout the entire application lifecycle.<n>System incorporates modern cloud security primitives including; role-based access control, centralized key management, encryption in transit and at rest, and controlled integration with cloud-based data processing services.
arXiv Detail & Related papers (2026-03-09T23:45:00Z) - CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
arXiv Detail & Related papers (2026-01-14T23:06:35Z) - Towards Verifiably Safe Tool Use for LLM Agents [53.55621104327779]
Large language model (LLM)-based AI agents extend capabilities by enabling access to tools such as data sources, APIs, search engines, code sandboxes, and even other agents.<n>LLMs may invoke unintended tool interactions and introduce risks, such as leaking sensitive data or overwriting critical records.<n>Current approaches to mitigate these risks, such as model-based safeguards, enhance agents' reliability but cannot guarantee system safety.
arXiv Detail & Related papers (2026-01-12T21:31:38Z) - Bridging the Mobile Trust Gap: A Zero Trust Framework for Consumer-Facing Applications [51.56484100374058]
This paper proposes an extended Zero Trust model designed for mobile applications operating in untrusted, user-controlled environments.<n>Using a design science methodology, the study introduced a six-pillar framework that supports runtime enforcement of trust.<n>The proposed model offers a practical and standards-aligned approach to securing mobile applications beyond pre-deployment controls.
arXiv Detail & Related papers (2025-08-20T18:42:36Z) - SkyEye: When Your Vision Reaches Beyond IAM Boundary Scope in AWS Cloud [0.0]
Cloud security has emerged as a primary concern for enterprises.<n> IAM constitutes the critical security backbone of most cloud deployments.<n>SkyEye is a cooperative multi-principal IAM enumeration framework.
arXiv Detail & Related papers (2025-07-01T01:36:52Z) - Progent: Programmable Privilege Control for LLM Agents [46.31581986508561]
We introduce Progent, the first privilege control framework to secure Large Language Models agents.<n>Progent enforces security at the tool level by restricting agents to performing tool calls necessary for user tasks while blocking potentially malicious ones.<n>Thanks to our modular design, integrating Progent does not alter agent internals and only requires minimal changes to the existing agent implementation.
arXiv Detail & Related papers (2025-04-16T01:58:40Z) - CloudLens: Modeling and Detecting Cloud Security Vulnerabilities [15.503757553097387]
Cloud computing services provide scalable and cost-effective solutions for data storage, processing, and collaboration.<n>Access control misconfigurations are often the primary driver for cloud attacks.<n>A planner generates attacks to identify such vulnerabilities in the cloud.
arXiv Detail & Related papers (2024-02-16T03:28:02Z) - Exploring Security Practices in Infrastructure as Code: An Empirical
Study [54.669404064111795]
Cloud computing has become popular thanks to the widespread use of Infrastructure as Code (IaC) tools.
scripting process does not automatically prevent practitioners from introducing misconfigurations, vulnerabilities, or privacy risks.
Ensuring security relies on practitioners understanding and the adoption of explicit policies, guidelines, or best practices.
arXiv Detail & Related papers (2023-08-07T23:43:32Z) - Not what you've signed up for: Compromising Real-World LLM-Integrated
Applications with Indirect Prompt Injection [64.67495502772866]
Large Language Models (LLMs) are increasingly being integrated into various applications.
We show how attackers can override original instructions and employed controls using Prompt Injection attacks.
We derive a comprehensive taxonomy from a computer security perspective to systematically investigate impacts and vulnerabilities.
arXiv Detail & Related papers (2023-02-23T17:14:38Z) - Using Constraint Programming and Graph Representation Learning for
Generating Interpretable Cloud Security Policies [12.43505973436359]
Cloud security relies on Identity Access Management (IAM) policies that IT admins need to properly configure and periodically update.
We develop a novel framework that encodes generating optimal IAM policies using constraint programming (CP)
We show that our optimized IAM policies significantly reduce the impact of security attacks using real data from 8 commercial organizations, and synthetic instances.
arXiv Detail & Related papers (2022-05-02T22:15:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.