Reducing Usefulness of Stolen Credentials in SSO Contexts
- URL: http://arxiv.org/abs/2401.11599v1
- Date: Sun, 21 Jan 2024 21:05:32 GMT
- Title: Reducing Usefulness of Stolen Credentials in SSO Contexts
- Authors: Sam Hays, Michael Sandborn, Dr. Jules White,
- Abstract summary: Multi-factor authentication (MFA) helps to thwart attacks that use valid credentials, but attackers still commonly breach systems by tricking users into accepting MFA step up requests.
This paper proposes a token-based enrollment architecture that is less invasive to user devices than mobile device management.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Approximately 61% of cyber attacks involve adversaries in possession of valid credentials. Attackers acquire credentials through various means, including phishing, dark web data drops, password reuse, etc. Multi-factor authentication (MFA) helps to thwart attacks that use valid credentials, but attackers still commonly breach systems by tricking users into accepting MFA step up requests through techniques, such as ``MFA Bombing'', where multiple requests are sent to a user until they accept one. Currently, there are several solutions to this problem, each with varying levels of security and increasing invasiveness on user devices. This paper proposes a token-based enrollment architecture that is less invasive to user devices than mobile device management, but still offers strong protection against use of stolen credentials and MFA attacks.
Related papers
- FATH: Authentication-based Test-time Defense against Indirect Prompt Injection Attacks [45.65210717380502]
Large language models (LLMs) have been widely deployed as the backbone with additional tools and text information for real-world applications.
prompt injection attacks are particularly threatening, where malicious instructions injected in the external text information can exploit LLMs to generate answers as the attackers desire.
This paper introduces a novel test-time defense strategy, named AuThentication with Hash-based tags (FATH)
arXiv Detail & Related papers (2024-10-28T20:02:47Z) - Excavating Vulnerabilities Lurking in Multi-Factor Authentication Protocols: A Systematic Security Analysis [2.729532849571912]
Single-factor authentication (SFA) protocols are often bypassed by side-channel and other attack techniques.
To alleviate this problem, multi-factor authentication (MFA) protocols have been widely adopted recently.
arXiv Detail & Related papers (2024-07-29T23:37:38Z) - A Passwordless MFA Utlizing Biometrics, Proximity and Contactless Communication [0.3749861135832073]
This paper introduces an advanced authentication method for user verification that utilizes the user's real-time facial biometric identity.
We have implemented a prototype authentication system on a BLE-NFC-enabled Android device, and initial threat modeling suggests that it is safe against known phishing attacks.
arXiv Detail & Related papers (2024-06-13T10:58:25Z) - Rethinking the Vulnerabilities of Face Recognition Systems:From a Practical Perspective [53.24281798458074]
Face Recognition Systems (FRS) have increasingly integrated into critical applications, including surveillance and user authentication.
Recent studies have revealed vulnerabilities in FRS to adversarial (e.g., adversarial patch attacks) and backdoor attacks (e.g., training data poisoning)
arXiv Detail & Related papers (2024-05-21T13:34:23Z) - From Hardware Fingerprint to Access Token: Enhancing the Authentication on IoT Devices [33.25850729215212]
We present MCU-Token, a secure hardware fingerprinting framework for MCU-based IoT devices.
MCU-Token can achieve high accuracy (over 97%) with a low overhead across various IoT devices and application scenarios.
arXiv Detail & Related papers (2024-03-22T15:15:28Z) - Evaluating the Influence of Multi-Factor Authentication and Recovery Settings on the Security and Accessibility of User Accounts [0.0]
This paper presents a study on the account settings of Google and Apple users.
Considering the multi-factor authentication configuration and recovery options, we analyzed the account security and lock-out risks.
Our results provide insights into the usage of multi-factor authentication in practice, show significant security differences between Google and Apple accounts, and reveal that many users would miss access to their accounts when losing a single authentication device.
arXiv Detail & Related papers (2024-03-22T10:05:37Z) - Poisoning Federated Recommender Systems with Fake Users [48.70867241987739]
Federated recommendation is a prominent use case within federated learning, yet it remains susceptible to various attacks.
We introduce a novel fake user based poisoning attack named PoisonFRS to promote the attacker-chosen targeted item.
Experiments on multiple real-world datasets demonstrate that PoisonFRS can effectively promote the attacker-chosen item to a large portion of genuine users.
arXiv Detail & Related papers (2024-02-18T16:34:12Z) - Invisible Backdoor Attack with Dynamic Triggers against Person
Re-identification [71.80885227961015]
Person Re-identification (ReID) has rapidly progressed with wide real-world applications, but also poses significant risks of adversarial attacks.
We propose a novel backdoor attack on ReID under a new all-to-unknown scenario, called Dynamic Triggers Invisible Backdoor Attack (DT-IBA)
We extensively validate the effectiveness and stealthiness of the proposed attack on benchmark datasets, and evaluate the effectiveness of several defense methods against our attack.
arXiv Detail & Related papers (2022-11-20T10:08:28Z) - mPSAuth: Privacy-Preserving and Scalable Authentication for Mobile Web
Applications [0.0]
mPSAuth is an approach for continuously tracking various data sources reflecting user behavior and estimating the likelihood of the current user being legitimate.
We show that mPSAuth can provide high accuracy with low encryption and communication overhead, while the effort for the inference is increased to a tolerable extent.
arXiv Detail & Related papers (2022-10-07T12:49:34Z) - Robust and Verifiable Information Embedding Attacks to Deep Neural
Networks via Error-Correcting Codes [81.85509264573948]
In the era of deep learning, a user often leverages a third-party machine learning tool to train a deep neural network (DNN) classifier.
In an information embedding attack, an attacker is the provider of a malicious third-party machine learning tool.
In this work, we aim to design information embedding attacks that are verifiable and robust against popular post-processing methods.
arXiv Detail & Related papers (2020-10-26T17:42:42Z) - Backdoor Attack against Speaker Verification [86.43395230456339]
We show that it is possible to inject the hidden backdoor for infecting speaker verification models by poisoning the training data.
We also demonstrate that existing backdoor attacks cannot be directly adopted in attacking speaker verification.
arXiv Detail & Related papers (2020-10-22T11:10:08Z)
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.