arXiv subjects

Nick Feamster

Publications and source records attributed to Nick Feamster.

At least 19 recordsLinked to original sources

arXiv2026

Rethinking Agent Security as a Networking Problem

AI agents are rapidly becoming more capable and widely deployed, promising substantial gains in productivity and enabling new classes of applications. However, their growing autonomy also introduces significant privacy and security risks. Existing defenses are predominantly agent-centric, relying on the agent itself to detect threats and enforce privacy and security policies. This approach is fundamentally limited because it entrusts policy enforcement to AI agents whose LLM-driven behavior is inherently nondeterministic and vulnerable to manipulation through attacks such as prompt injection. As a result, current defenses cannot reliably prevent privacy and security threats, highlighting a critical need for a new solution to securing AI agent systems. The networking community has long grappled with similar challenges and offers insightful principles we can borrow to design a more secure AI agent system. These include centralized control with distributed enforcement, capability-based access for mediating requests to sensitive resources, and least privilege through zero-trust enforcement. Historically, these principles have provided strong deterministic guarantees for networked systems. However, these principles alone are insufficient for AI agents because the safety and appropriateness of an agent's actions often depend on semantic context beyond the expressiveness of static rules. Building on these principles, we advocate for a systematic approach to AI agent security that combines deterministic enforcement mechanisms, which provide strong security guarantees, with semantic, context-aware policies that enable nuanced decision-making. We then present a reference architecture and identify key research questions and future directions to guide the design of secure and privacy-preserving AI agent systems.

cs.MA↗

arXiv2026

An Internet for the KV Cache: Rethinking Classical Infrastructure Boundaries in the LLM Inference Age

LLM inference has become a global-scale, heterogeneous workload spanning agents, retrieval, tool-use, code execution and multi-modal reasoning. These workloads naturally enable context reuse from overlapping inputs, creating a major opportunity to store and reuse the contexts' KV Caches instead of recomputing them. However, model-side advances that shrink the KV Cache and system-side advances that reduce compute, storage, and transfer costs are evolve independently within legacy cloud boundaries. We argue that future inference infrastructure should allow decoupling of compute and KV Cache storage across cloud and datacenters. The network becomes an active distribution channel; bandwidth, latency and pricing directly determines how the KV Cache should be managed. We propose a vision for an Internet for the KV Cache, with KV Cache management working as a content-distribution system. In this view, KV Cache storage and recompute decisions are driven by model, infrastructure, and application metrics, to enable adaptive, content-driven decisions for minimizing latency and cost.

cs.NI↗

arXiv2026

Measuring, Localizing, and Ablating Alignment Signatures in LLMs

Aligned language models often exhibit a recognizable AI-like style, yet its connection to post-training and internal representations remains poorly understood. In this work, we study whether post-training introduces or amplifies AI-like stylistic regularities and whether these regularities have a localized internal signature. To this end, we compare human text, base-model generations, and aligned-model generations under matched human-source prefixes. Aligned generations show lower human-corpus affinity and higher AI-detection rates than base generations, suggesting that post-training shifts generated text away from human-corpus style and toward detector-visible AI-like text. We then introduce PASTA (Post-training Alignment Signature Targeted Ablation), a training-free method that estimates a post-training alignment signature from aligned-base residual contrasts and ablates the corresponding direction during decoding. Across 11 aligned models and 6 AI detectors, PASTA lowers the detection rate for most aligned models; this effect transfers well across detectors and is not reproduced by random directions. Qualitative analysis suggests that PASTA generations remain relevant and coherent while exhibiting greater stylistic variation. Together, these results show that AI-like stylistic effects of post-training can be measured, localized, and causally tested through activation ablation.

cs.LG↗

arXiv2026

Governance of AI-Generated Content: A Case Study on Social Media Platforms

Online platforms are seeing increasing amounts of AI-generated content -- text and other forms of media that are made or co-created with generative AI. This trend suggests platforms may need to establish governance frameworks, including policies and enforcement strategies for how users create, post, share, and engage with such content to encourage responsible use. We investigate the governance of AI-generated content across 40 popular social media platforms. Just over two-thirds explicitly describe governance of AI-generated content spanning six themes. Most platforms focus on moderating AI-generated content that violates established content rules and discloses AI-generated content. Fewer platforms -- those that are focused on creativity and knowledge-sharing -- address other issues such as ownership and monetization. Based on these findings, we suggest stakeholders and policymakers develop more direct, comprehensive, and forward-looking AI-generated content governance, as well as tools and education for users about the use of such content.

cs.HC↗

arXiv2026

Less is More: Optimizing Probe Selection Using Shared Latency Anomalies

Latency anomalies, defined as persistent or transient increases in round-trip time (RTT), are common in residential Internet performance. When multiple users observe anomalies to the same destination, this may reflect shared infrastructure, routing behavior, or congestion. Inferring such shared behavior is challenging because anomaly magnitudes vary widely across devices, even within the same ISP and geographic area, and detailed network topology information is often unavailable. We study whether devices experiencing a shared latency anomaly observe similar changes in RTT magnitude using a topology-agnostic approach. Using four months of high-frequency RTT measurements from 99 residential probes in Chicago, we detect shared anomalies and analyze their consistency in amplitude and duration without relying on traceroutes or explicit path information. Building on prior change-point detection techniques, we find that many shared anomalies exhibit similar amplitude across users, particularly within the same ISP. Motivated by this observation, we design a sampling algorithm that reduces redundancy by selecting representative devices under user-defined constraints. Our approach captures 95 percent of aggregate anomaly impact using fewer than half of the deployed probes. Compared to two baselines, it identifies significantly more unique anomalies at comparable coverage levels. We further show that geographic diversity remains important when selecting probes within a single ISP, even at city scale. Overall, our results demonstrate that anomaly amplitude and duration provide effective topology-independent signals for scalable monitoring, troubleshooting, and cost-efficient sampling in residential Internet measurement.

cs.NI↗

arXiv2025

Quantifying the Privacy Implications of High-Fidelity Synthetic Network Traffic

To address the scarcity and privacy concerns of network traffic data, various generative models have been developed to produce synthetic traffic. However, synthetic traffic is not inherently privacy-preserving, and the extent to which it leaks sensitive information, and how to measure such leakage, remain largely unexplored. This challenge is further compounded by the diversity of model architectures, which shape how traffic is represented and synthesized. We introduce a comprehensive set of privacy metrics for synthetic network traffic, combining standard approaches like membership inference attacks (MIA) and data extraction attacks with network-specific identifiers and attributes. Using these metrics, we systematically evaluate the vulnerability of different representative generative models and examine the factors that influence attack success. Our results reveal substantial variability in privacy risks across models and datasets. MIA success ranges from 0% to 88%, and up to 100% of network identifiers can be recovered from generated traffic, highlighting serious privacy vulnerabilities. We further identify key factors that significantly affect attack outcomes, including training data diversity and how well the generative model fits the training data. These findings provide actionable guidance for designing and deploying generative models that minimize privacy leakage, establishing a foundation for safer synthetic network traffic generation.

cs.AI↗

arXiv2025

Characterizing the Impact of Active Queue Management on Speed Test Measurements

Present day speed test tools measure peak throughput, but often fail to capture the user-perceived responsiveness of a network connection under load. Recently, platforms such as NDT, Ookla Speedtest and Cloudflare Speed Test have introduced metrics such as ``latency under load'' or ``working latency'' to fill this gap. Yet, the sensitivity of these metrics to basic network configurations such as Active Queue Management (AQM) remains poorly understood. In this work, we conduct an empirical study of the impact of AQM on speed test measurements in a laboratory setting. Using controlled experiments, we compare the distribution of throughput and latency under different load measurements across different AQM schemes, including CoDel, FQ-CoDel and Stochastic Fair Queuing (SFQ). On comparing with a standard drop-tail baseline, we find that measurements have high variance across AQM schemes and load conditions. These results highlight the critical role of AQM in shaping how emerging latency metrics should be interpreted, and underscore the need for careful calibration of speed test platforms before their results are used to guide policy or regulatory outcomes.

cs.NI↗

arXiv2025

Can LLMs Address Mental Health Questions? A Comparison with Human Therapists

Limited access to mental health care has motivated the use of digital tools and conversational agents powered by large language models (LLMs), yet their quality and reception remain unclear. We present a study comparing therapist-written responses to those generated by ChatGPT, Gemini, and Llama for real patient questions. Text analysis showed that LLMs produced longer, more readable, and lexically richer responses with a more positive tone, while therapist responses were more often written in the first person. In a survey with 150 users and 23 licensed therapists, participants rated LLM responses as clearer, more respectful, and more supportive than therapist-written answers. Yet, both groups of participants expressed a stronger preference for human therapist support. These findings highlight the promise and limitations of LLMs in mental health, underscoring the need for designs that balance their communicative strengths with concerns of trust, privacy, and accountability.

cs.HC↗

arXiv2025

Beyond PII: How Users Attempt to Estimate and Mitigate Implicit LLM Inference

Large Language Models (LLMs) such as ChatGPT can infer personal attributes from seemingly innocuous text, raising privacy risks beyond memorized data leakage. While prior work has demonstrated these risks, little is known about how users estimate and respond. We conducted a survey with 240 U.S. participants who judged text snippets for inference risks, reported concern levels, and attempted rewrites to block inference. We compared their rewrites with those generated by ChatGPT and Rescriber, a state-of-the-art sanitization tool. Results show that participants struggled to anticipate inference, performing a little better than chance. User rewrites were effective in just 28\% of cases - better than Rescriber but worse than ChatGPT. We examined our participants' rewriting strategies, and observed that while paraphrasing was the most common strategy it is also the least effective; instead abstraction and adding ambiguity were more successful. Our work highlights the importance of inference-aware design in LLM interactions.

cs.HC↗

arXiv2025

WiFinger: Fingerprinting Noisy IoT Event Traffic Using Packet-level Sequence Matching

IoT environments such as smart homes are susceptible to privacy inference attacks, where attackers can analyze patterns of encrypted network traffic to infer the state of devices and even the activities of people. While most existing attacks exploit ML techniques for discovering such traffic patterns, they underperform on wireless traffic, especially Wi-Fi, due to its heavy noisiness and the packet loss of wireless sniffing. In addition, these approaches commonly target distinguishing chunked IoT event traffic samples, and they fail at effectively tracking multiple events simultaneously. In this work, we propose WiFinger, a fine-grained multi-IoT event fingerprinting approach against noisy traffic. WiFinger turns the traffic pattern classification task into a subsequence matching problem and introduces novel techniques to account for the high time complexity while maintaining high accuracy. In addition, its reliance on training sample volumes reduces efforts for any future fingerprint updates. Experiments demonstrate that WiFinger outperforms existing approaches under practical threat models, with an average recall of 89% (v.s. 49% and 46% respectively) and almost zero false positives for various IoT events.

cs.CR↗

arXiv2025

"I Cannot Write This Because It Violates Our Content Policy": Understanding Content Moderation Policies and User Experiences in Generative AI Products

While recent research has focused on developing safeguards for generative AI (GAI) model-level content safety, little is known about how content moderation to prevent malicious content performs for end-users in real-world GAI products. To bridge this gap, we investigated content moderation policies and their enforcement in GAI online tools -- consumer-facing web-based GAI applications. We first analyzed content moderation policies of 14 GAI online tools. While these policies are comprehensive in outlining moderation practices, they usually lack details on practical implementations and are not specific about how users can aid in moderation or appeal moderation decisions. Next, we examined user-experienced content moderation successes and failures through Reddit discussions on GAI online tools. We found that although moderation systems succeeded in blocking malicious generations pervasively, users frequently experienced frustration in failures of both moderation systems and user support after moderation. Based on these findings, we suggest improvements for content moderation policy and user experiences in real-world GAI products.

cs.HC↗

arXiv2025

NetSSM: Multi-Flow and State-Aware Network Trace Generation using State Space Models

Access to raw network traffic data is essential for many computer networking tasks, from traffic modeling to performance evaluation. Unfortunately, this data is scarce due to high collection costs and governance rules. Previous efforts explore this challenge by generating synthetic network data, but fail to reliably handle multi-flow sessions, struggle to reason about stateful communication in moderate to long-duration network sessions, and lack robust evaluations tied to real-world utility. We propose a new method based on state space models called NetSSM that generates raw network traffic at the packet-level granularity. Our approach captures interactions between multiple, interleaved flows -- an objective unexplored in prior work -- and effectively reasons about flow-state in sessions to capture traffic characteristics. NetSSM accomplishes this by learning from and producing traces 8x and 78x longer than existing transformer-based approaches. Evaluation results show that our method generates high-fidelity traces that outperform prior efforts in existing benchmarks. We also find that NetSSM's traces have high semantic similarity to real network data regarding compliance with standard protocol requirements and flow and session-level traffic characteristics.

cs.NI↗

arXiv2025

Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams

Machine learning can analyze vast amounts of data generated by IoT devices to identify patterns, make predictions, and enable real-time decision-making. By processing sensor data, machine learning models can optimize processes, improve efficiency, and enhance personalized user experiences in smart systems. However, IoT systems are often deployed in sensitive environments such as households and offices, where they may inadvertently expose identifiable information, including location, habits, and personal identifiers. This raises significant privacy concerns, necessitating the application of data minimization -- a foundational principle in emerging data regulations, which mandates that service providers only collect data that is directly relevant and necessary for a specified purpose. Despite its importance, data minimization lacks a precise technical definition in the context of sensor data, where collections of weak signals make it challenging to apply a binary "relevant and necessary" rule. This paper provides a technical interpretation of data minimization in the context of sensor streams, explores practical methods for implementation, and addresses the challenges involved. Through our approach, we demonstrate that our framework can reduce user identifiability by up to 16.7% while maintaining accuracy loss below 1%, offering a viable path toward privacy-preserving IoT data processing.

cs.LG↗

arXiv2025

Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection

Machine learning has shown promise in network intrusion detection systems, yet its performance often degrades due to concept drift and imbalanced data. These challenges are compounded by the labor-intensive process of labeling network traffic, especially when dealing with evolving and rare attack types, which makes preparing the right data for adaptation difficult. To address these issues, we propose a generative active adaptation framework that minimizes labeling effort while enhancing model robustness. Our approach employs density-aware dataset prior selection to identify the most informative samples for annotation, and leverages deep generative models to conditionally synthesize diverse samples, thereby augmenting the training set and mitigating the effects of concept drift. We evaluate our end-to-end framework \NetGuard on both simulated IDS data and a real-world ISP dataset, demonstrating significant improvements in intrusion detection performance. Our method boosts the overall F1-score from 0.60 (without adaptation) to 0.86. Rare attacks such as Infiltration, Web Attack, and FTP-BruteForce, which originally achieved F1 scores of 0.001, 0.04, and 0.00, improve to 0.30, 0.50, and 0.71, respectively, with generative active adaptation in the CIC-IDS 2018 dataset. Our framework effectively enhances rare attack detection while reducing labeling costs, making it a scalable and practical solution for intrusion detection.

cs.NI↗

arXiv2025

Towards On-Device Evidence Gathering for Intimate Partner Infiltration: A Feasibility Study for Joint Identity-Action Detection

Intimate Partner Infiltration (IPI) refers to phone-side privacy infiltration in intimate or close relationships, often enabled by physical access to a person's smartphone and discussed in technology-facilitated Intimate Partner Violence (IPV) contexts. Unlike conventional cyberattackers, IPI perpetrators leverage proximity and personal knowledge to circumvent standard protection, underscoring the need for targeted interventions, motivating device-side tools that surface such risk evidence for later review. While prior works have extensively studied IPV, and some have provided tailored and effective solutions such as security clinics, they are necessarily episodic and human-expert-intensive, and offer limited automated visibility into what happens on a smartphone between support sessions. Guided by a formative interview with experts (n=5), we take the first exploration into gathering IPI-risk evidence from a mobile system perspective and present AID, Automated IPI Detection, a data-driven system that continuously logs unauthorized access and suspicious behaviors on smartphones. In a controlled 27-participant study, AID achieves an end-to-end F1 score of 0.928 with a 7.0% false positive rate for Top-1 phone-side risk flagging; when preserving top-3 candidate action categories as report context, AID achieves an F1 score of 0.981 and a false positive rate of 1.6%. These findings demonstrate AID's potential as an evidence-support tool that complements current clinic-based interpretation and safety-planning.

cs.CR↗

arXiv2024

CAIP: Detecting Router Misconfigurations with Context-Aware Iterative Prompting of LLMs

Model checkers and consistency checkers detect critical errors in router configurations, but these tools require significant manual effort to develop and maintain. LLM-based Q&A models have emerged as a promising alternative, allowing users to query partitions of configurations through prompts and receive answers based on learned patterns, thanks to transformer models pre-trained on vast datasets that provide generic configuration context for interpreting router configurations. Yet, current methods of partition-based prompting often do not provide enough network-specific context from the actual configurations to enable accurate inference. We introduce a Context-Aware Iterative Prompting (CAIP) framework that automates network-specific context extraction and optimizes LLM prompts for more precise router misconfiguration detection. CAIP addresses three challenges: (1) efficiently mining relevant context from complex configuration files, (2) accurately distinguishing between pre-defined and user-defined parameter values to prevent irrelevant context from being introduced, and (3) managing prompt context overload with iterative, guided interactions with the model. Our evaluations on synthetic and real-world configurations show that CAIP improves misconfiguration detection accuracy by more than 30% compared to partition-based LLM approaches, model checkers, and consistency checkers, uncovering over 20 previously undetected misconfigurations in real-world configurations.

cs.NI↗

arXiv2024

MYCROFT: Towards Effective and Efficient External Data Augmentation

Machine learning (ML) models often require large amounts of data to perform well. When the available data is limited, model trainers may need to acquire more data from external sources. Often, useful data is held by private entities who are hesitant to share their data due to propriety and privacy concerns. This makes it challenging and expensive for model trainers to acquire the data they need to improve model performance. To address this challenge, we propose Mycroft, a data-efficient method that enables model trainers to evaluate the relative utility of different data sources while working with a constrained data-sharing budget. By leveraging feature space distances and gradient matching, Mycroft identifies small but informative data subsets from each owner, allowing model trainers to maximize performance with minimal data exposure. Experimental results across four tasks in two domains show that Mycroft converges rapidly to the performance of the full-information baseline, where all data is shared. Moreover, Mycroft is robust to noise and can effectively rank data owners by utility. Mycroft can pave the way for democratized training of high performance ML models.

cs.LG↗

arXiv2024

SwiftQueue: Optimizing Low-Latency Applications with Swift Packet Queuing

Low Latency, Low Loss, and Scalable Throughput (L4S), as an emerging router-queue management technique, has seen steady deployment in the industry. An L4S-enabled router assigns each packet to the queue based on the packet header marking. Currently, L4S employs per-flow queue selection, i.e. all packets of a flow are marked the same way and thus use the same queues, even though each packet is marked separately. However, this may hurt tail latency and latency-sensitive applications because transient congestion and queue buildups may only affect a fraction of packets in a flow. We present SwiftQueue, a new L4S queue-selection strategy in which a sender uses a novel per-packet latency predictor to pinpoint which packets likely have latency spikes or drops. The insight is that many packet-level latency variations result from complex interactions among recent packets at shared router queues. Yet, these intricate packet-level latency patterns are hard to learn efficiently by traditional models. Instead, SwiftQueue uses a custom Transformer, which is well-studied for its expressiveness on sequential patterns, to predict the next packet's latency based on the latencies of recently received ACKs. Based on the predicted latency of each outgoing packet, SwiftQueue's sender dynamically marks the L4S packet header to assign packets to potentially different queues, even within the same flow. Using real network traces, we show that SwiftQueue is 45-65% more accurate in predicting latency and its variations than state-of-art methods. Based on its latency prediction, SwiftQueue reduces the tail latency for L4S-enabled flows by 36-45%, compared with the existing L4S queue-selection method.

cs.NI↗