Ask for Alice: Online Covert Distress Signal in the Presence of a Strong Adversary
- URL: http://arxiv.org/abs/2310.03237v1
- Date: Thu, 5 Oct 2023 01:07:06 GMT
- Title: Ask for Alice: Online Covert Distress Signal in the Presence of a Strong Adversary
- Authors: Hayyu Imanda, Kasper Rasmussen,
- Abstract summary: This allows a user to call for help even when they are in the same physical space as their adversaries.
Our model fits into scenarios where a user is under surveillance and wishes to inform a trusted party of the situation.
- Score: 1.433758865948252
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper we propose a protocol that can be used to covertly send a distress signal through a seemingly normal webserver, even if the adversary is monitoring both the network and the user's device. This allows a user to call for help even when they are in the same physical space as their adversaries. We model such a scenario by introducing a strong adversary model that captures a high degree of access to the user's device and full control over the network. Our model fits into scenarios where a user is under surveillance and wishes to inform a trusted party of the situation. To do this, our method uses existing websites to act as intermediaries between the user and a trusted backend; this enables the user to initiate the distress signal without arousing suspicion, even while being actively monitored. We accomplish this by utilising the TLS handshake to convey additional information; this means that any website wishing to participate can do so with minimal effort and anyone monitoring the traffic will just see common TLS connections. In order for websites to be willing to host such a functionality the protocol must coexist gracefully with users who use normal TLS and the computational overhead must be minimal. We provide a full security analysis of the architecture and prove that the adversary cannot distinguish between a set of communications which contains a distress call and a normal communication.
Related papers
- Defending Against Attack on the Cloned: In-Band Active Man-in-the-Middle Detection for the Signal Protocol [1.6114012813668932]
We present a solution to active Man-in-the-Middle (MitM) attacks on Signal.
Our solution automates the process of key confirmation without relying on the intervention of users.
We document the new security guarantees while preserving the existing security guarantees of Signal.
arXiv Detail & Related papers (2024-10-21T15:23:58Z) - Secure Semantic Communication via Paired Adversarial Residual Networks [59.468221305630784]
This letter explores the positive side of the adversarial attack for the security-aware semantic communication system.
A pair of matching pluggable modules is installed: one after the semantic transmitter and the other before the semantic receiver.
The proposed scheme is capable of fooling the eavesdropper while maintaining the high-quality semantic communication.
arXiv Detail & Related papers (2024-07-02T08:32:20Z) - EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection [53.25863925815954]
Federated self-supervised learning (FSSL) has emerged as a promising paradigm that enables the exploitation of clients' vast amounts of unlabeled data.
While FSSL offers advantages, its susceptibility to backdoor attacks has not been investigated.
We propose the Embedding Inspector (EmInspector) that detects malicious clients by inspecting the embedding space of local models.
arXiv Detail & Related papers (2024-05-21T06:14:49Z) - The Key to Deobfuscation is Pattern of Life, not Overcoming Encryption [0.7124736158080939]
We present a novel methodology that is effective at deobfuscating sources by synthesizing measurements from key locations along protocol transaction paths.
Our approach links online personas with their origin IP addresses based on a Pattern of Life (PoL) analysis.
We show that, when monitoring in the correct places on the Internet, DNS over HTTPS (DoH) and DNS over TLS (DoT) can be deobfuscated with up to 100% accuracy.
arXiv Detail & Related papers (2023-10-04T02:34:29Z) - 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) - Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers.
Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods.
Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
arXiv Detail & Related papers (2022-07-20T19:49:09Z) - Certifiably Robust Policy Learning against Adversarial Communication in
Multi-agent Systems [51.6210785955659]
Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions.
However, when deploying trained communicative agents in a real-world application where noise and potential attackers exist, the safety of communication-based policies becomes a severe issue that is underexplored.
In this work, we consider an environment with $N$ agents, where the attacker may arbitrarily change the communication from any $CfracN-12$ agents to a victim agent.
arXiv Detail & Related papers (2022-06-21T07:32:18Z) - Masked LARk: Masked Learning, Aggregation and Reporting worKflow [6.484847460164177]
Many web advertising data flows involve passive cross-site tracking of users.
Most browsers are moving towards removal of 3PC in subsequent browser iterations.
We propose a new proposal, called Masked LARk, for aggregation of user engagement measurement and model training.
arXiv Detail & Related papers (2021-10-27T21:59:37Z) - Adversarial Attacks On Multi-Agent Communication [80.4392160849506]
Modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems.
Such advantages rely heavily on communication channels which have been shown to be vulnerable to security breaches.
In this paper, we explore such adversarial attacks in a novel multi-agent setting where agents communicate by sharing learned intermediate representations.
arXiv Detail & Related papers (2021-01-17T00:35:26Z) - Machine Learning Interpretability Meets TLS Fingerprinting [5.179808182296037]
We propose a framework to systematically find the most vulnerable information fields in a network protocol.
focusing on the transport layer security (TLS) protocol, we perform different machine-learning-based fingerprinting attacks on the collected data.
By employing the interpretation techniques developed in the machine learning community and applying our framework, we find the most vulnerable information fields in the TLS protocol.
arXiv Detail & Related papers (2020-11-12T10:37:45Z) - Adaptive Webpage Fingerprinting from TLS Traces [13.009834690757614]
In webpage fingerprinting, an adversary infers the specific webpage loaded by a victim user by analysing the patterns in the encrypted TLS traffic exchanged between the user's browser and the website's servers.
This work studies modern webpage fingerprinting adversaries against the TLS protocol.
We introduce a TLS-specific model that: 1) scales to an unprecedented number of target webpages, 2) can accurately classify thousands of classes it never encountered during training, and 3) has low operational costs even in scenarios of frequent page updates.
arXiv Detail & Related papers (2020-10-19T15:13:07Z)
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.