Overprivilege Analysis of Security Policies in Serverless Cloud Applications
- URL: http://arxiv.org/abs/2607.02875v1
- Date: Fri, 03 Jul 2026 02:22:14 GMT
- Title: Overprivilege Analysis of Security Policies in Serverless Cloud Applications
- Abstract summary: 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.
- Score: 1.3068208613904335
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Serverless computing has seen rapid adoption in cloud deployments, yet the security implications of its service-oriented programming model remain poorly understood. Distributed, modular, and heterogeneous applications complicate the specification of precise security policies. Role-based access control solutions such as Identity and Access Management (IAM) already exhibit pervasive misconfiguration problems, and the multiplicity of functions, services, and resources in serverless applications, together with frequent permission model changes by cloud providers, greatly increases the likelihood of policy misconfigurations. Consequently, policies are often overprivileged, thereby enlarging the attack surface and exposing sensitive cloud resources to compromise. We present a large-scale measurement study of overprivilege in real-world serverless applications, analyzing a curated dataset of 689 AWS Lambda applications comprised of 1,293 functions. To enable this study, we develop PrivLess, a static policy analysis framework that extracts function-to-resource interactions from application source code, derives an interaction-permission mapping, and reconciles inferred interactions with declared policies to quantify overprivilege. Our measurement reveals that overprivilege is systemic and severe across the serverless ecosystem: 47.7% of applications carry excess permissions with a significant privilege reduction potential of 99.65%. Applications with wildcard-defined permissions exhibited an average overprivilege ratio 274x higher than those without. More critically, the excess permissions enable concrete attack vectors: 18.8% of applications hold unnecessary Privilege Escalation capabilities, and 12 applications had Defense Evasion permissions they did not need.
Related papers
- Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale [54.83971397981572]
Language-model agents increasingly operate over complete software repositories.<n> cybersecurity evaluations measure whether they can detect, reproduce, or repair vulnerabilities rather than whether they can locate the relevant code.<n>We study vulnerability localization: given a weakness class and an unfamiliar repository, identify the implementation files associated with that weakness.
arXiv Detail & Related papers (2026-09-14T17:44:51Z) - Understanding and Mitigating Prompt Leaking Attacks in Real-World LLM-Based Applications [57.33208520608409]
This paper presents a systematic study of prompt leaking in large language model (LLM)-based applications.<n>We find that over 80% of deployments leak system prompts under realistic adversarial queries.<n>We propose AREA, a practical defense that re-anchors the model's attention using an optimizable soft prompt.
arXiv Detail & Related papers (2026-06-17T04:20:00Z) - Detecting Privilege Escalation in Polyglot Microservices via Agentic Program Analysis [31.42091263566326]
We present Neo, an agentic program analysis framework that combines large language models (LLMs) with classic program analysis.<n>We evaluate Neo on 25 open-source microservice applications spanning 7 programming languages and 6.2 million lines of code.<n>Neo uncovered 24 zero-day privilege escalation vulnerabilities and achieved 81.0% precision and 85.0% recall on a ground-truth dataset.
arXiv Detail & Related papers (2026-05-15T03:27:02Z) - ALPS: Automated Least-Privilege Enforcement for Securing Serverless Functions [3.3679122194886872]
We present ALPS, an automated framework for enforcing least privilege in serverless environments.<n>Our system employs serverless-tailored static analysis to extract precise permission requirements from function code.<n>It also performs real-time monitoring to block unauthorized access and adapt to policy or code changes.
arXiv Detail & Related papers (2026-03-26T12:39:58Z) - Paladin: A Policy Framework for Securing Cloud APIs by Combining Application Context with Generative AI [0.16469845434482486]
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.
arXiv Detail & Related papers (2026-03-10T20:55:48Z) - Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition [101.86739402748995]
We run the largest public red-teaming competition to date, targeting 22 frontier AI agents across 44 realistic deployment scenarios.<n>We build the Agent Red Teaming benchmark and evaluate it across 19 state-of-the-art models.<n>Our findings highlight critical and persistent vulnerabilities in today's AI agents.
arXiv Detail & Related papers (2025-07-28T05:13:04Z) - Unveiling the Landscape of LLM Deployment in the Wild: An Empirical Study [7.5198516000202105]
Large language models (LLMs) are increasingly deployed through open-source and commercial frameworks.<n>As LLM deployments become prevalent, particularly in industry, ensuring their secure and reliable operation has become a critical issue.<n>Insecure defaults and misconfigurations often expose LLM services to the public internet, posing serious security and system engineering risks.
arXiv Detail & Related papers (2025-05-05T09:30:19Z) - 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) - Analyzing Maintenance Activities of Software Libraries [55.2480439325792]
Industrial applications heavily integrate open-source software libraries nowadays.<n>I want to introduce an automatic monitoring approach for industrial applications to identify open-source dependencies that show negative signs regarding their current or future maintenance activities.
arXiv Detail & Related papers (2023-06-09T16:51:25Z) - 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.