Maria Apostolaki

Constantine Doumanidis, Maria Apostolaki

With the promise of greater decentralization and sustainability, Ethereum transitioned from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism. The new consensus protocol introduces novel vulnerabilities that warrant further investigation. The goal of this paper is to investigate the security of Ethereum's PoS system from an Internet routing perspective. To this end, this paper makes two contributions: First, we devise a novel framework for inferring the distribution of validators on the Internet without disturbing the real network. Second, we introduce a class of network-level attacks on Ethereum's PoS system that jointly exploit Internet routing vulnerabilities with the protocol's reward and penalty mechanisms. We describe two representative attacks: StakeBleed, where the attacker triggers an inactivity leak, halting block finality and causing financial losses for all validators; and KnockBlock, where the attacker increases her expected MEV gains by preventing targeted blocks from being included in the chain. We find that both attacks are practical and effective. An attacker executing StakeBleed can inflict losses of almost 300 ETH in just 2 hours by hijacking as few as 30 IP prefixes. An attacker implementing KnockBlock could increase their MEV expected gains by 44.5% while hijacking a single prefix for less than 2 minutes. Our paper serves as a call to action for validators to reinforce their Internet routing infrastructure and for the Ethereum P2P protocol to implement stronger mechanisms to conceal validator locations.

Minhao Jin, Hongyu Hè, Maria Apostolaki

Synthetic network data generators (SynNetGens) are increasingly used to share realistic traffic traces without exposing sensitive raw data. While substantial effort has gone into improving fidelity, privacy is either assumed to be a built-in property of synthesis or addressed through differential privacy at the packet or flow level. This paper uncovers a fundamental privacy vulnerability: SynNetGens preserve cross-flow behavioral correlations that expose source-level membership, allowing an attacker to determine whether traffic of specific user, or service was included in the training data. This leakage arises from a mismatch in abstraction: existing SynNetGens operate and are protected at the packet or flow level, while sensitive information is encoded in correlations across flows from the same source. To demonstrate that this vulnerability is exploitable in practice, we develop TraceBleed, the first source-level membership inference attack against black-box SynNetGens. Our evaluation spans five datasets and six SynNetGens, revealing that: (i) every generator leaks source-level information on at least some datasets; (ii) flow- or packet-level differential privacy fails to protect source privacy unless fidelity is degraded to unusable levels; and (iii) releasing 10X more synthetic data amplifies leakage by 130% on average. To support ongoing research in this area, we will maintain a public privacy-fidelity leaderboard so practitioners can choose generators that fit their needs and researchers can benchmark new designs faithfully.

Hongyu Hè, Minhao Jin, Maria Apostolaki

RL-based controllers achieve strong average-case performance in networking tasks such as congestion control and adaptive bitrate streaming. Yet their performance can degrade severely under network conditions where strong performance is still achievable. Identifying such conditions and quantifying the resulting performance gap is intractable by enumeration, while the sequential and closed-loop nature of RL controllers makes formal verification methods impractical. We present ReGuard, a framework that discovers worst-case scenarios for a given RL controller and protects it against them at inference time without retraining. Discovery is formulated as a bilevel regret-maximization problem, which yields a certified lower bound on the worst-case performance gap. The discovered trajectories are then analyzed as counterfactuals and compiled into lightweight logic rules that intervene only when a risky state is detected, leaving the controller's behavior unchanged otherwise. We evaluate ReGuard across three RL-based network controllers: Pensieve, Sage, and Park. ReGuard discovers scenarios in which the controller's performance is 43$-$64% worse than what is achievable. ReGuard not only discovers gaps 57% to 6$\times$ larger than those found by the strongest baselines but also shrinks them by 79$-$85% via lightweight rule-based protection while preserving nominal performance. ReGuard's protection extends beyond the scenarios it discovers, improving performance across a wider range of network conditions.

Hongyu Hè, Minhao Jin, Maria Apostolaki

Generative ML models are increasingly popular in networking for tasks such as telemetry imputation, prediction, and synthetic trace generation. Despite their capabilities, they suffer from two shortcomings: (i) their output is often visibly violating well-known networking rules, which undermines their trustworthiness; and (ii) they are difficult to control, frequently requiring retraining even for minor changes. To address these limitations and unlock the benefits of generative models for networking, we propose a new paradigm for integrating explicit network knowledge in the form of first-order logic rules into ML models used for networking tasks. Rules capture well-known relationships among used signals, e.g., that increased latency precedes packet loss. While the idea is conceptually straightforward, its realization is challenging: networking knowledge is rarely formalized into rules, and naively injecting them into ML models often hampers ML's effectiveness. This paper introduces NetNomos a multi-stage framework that (1) learns rules directly from data (e.g., measurements); (2) filters them to distinguish semantically meaningful ones; and (3) enforces them through a collaborative generation between an ML model and an SMT solver.

Yixin Sun, Maria Apostolaki, Henry Birge-Lee et al.

Attacks on Internet routing are typically viewed through the lens of availability and confidentiality, assuming an adversary that either discards traffic or performs eavesdropping. Yet, a strategic adversary can use routing attacks to compromise the security of critical Internet applications like Tor, certificate authorities, and the bitcoin network. In this paper, we survey such application-specific routing attacks and argue that both application-layer and network-layer defenses are essential and urgently needed. While application-layer defenses are easier to deploy in the short term, we hope that our work serves to provide much needed momentum for the deployment of network-layer defenses.

Maria Apostolaki, Gian Marti, Jan Müller et al.

Routing attacks remain practically effective in the Internet today as existing countermeasures either fail to provide protection guarantees or are not easily deployable. Blockchain systems are particularly vulnerable to such attacks as they rely on Internet-wide communication to reach consensus. In particular, Bitcoin -the most widely-used cryptocurrency- can be split in half by any AS-level adversary using BGP hijacking. In this paper, we present SABRE, a secure and scalable Bitcoin relay network which relays blocks worldwide through a set of connections that are resilient to routing attacks. SABRE runs alongside the existing peer-to-peer network and is easily deployable. As a critical system, SABRE design is highly resilient and can efficiently handle high bandwidth loads, including Denial of Service attacks. We built SABRE around two key technical insights. First, we leverage fundamental properties of inter-domain routing (BGP) policies to host relay nodes: (i) in locations that are inherently protected against routing attacks; and (ii) on paths that are economically preferred by the majority of Bitcoin clients. These properties are generic and can be used to protect other Blockchain-based systems. Second, we leverage the fact that relaying blocks is communication-heavy, not computation-heavy. This enables us to offload most of the relay operations to programmable network hardware (using the P4 programming language). Thanks to this hardware/software co-design, SABRE nodes operate seamlessly under high load while mitigating the effects of malicious clients. We present a complete implementation of SABRE together with an extensive evaluation. Our results demonstrate that SABRE is effective at securing Bitcoin against routing attacks, even with deployments as small as 6 nodes.

Maria Apostolaki, Aviv Zohar, Laurent Vanbever

As the most successful cryptocurrency to date, Bitcoin constitutes a target of choice for attackers. While many attack vectors have already been uncovered, one important vector has been left out though: attacking the currency via the Internet routing infrastructure itself. Indeed, by manipulating routing advertisements (BGP hijacks) or by naturally intercepting traffic, Autonomous Systems (ASes) can intercept and manipulate a large fraction of Bitcoin traffic. This paper presents the first taxonomy of routing attacks and their impact on Bitcoin, considering both small-scale attacks, targeting individual nodes, and large-scale attacks, targeting the network as a whole. While challenging, we show that two key properties make routing attacks practical: (i) the efficiency of routing manipulation; and (ii) the significant centralization of Bitcoin in terms of mining and routing. Specifically, we find that any network attacker can hijack few (<100) BGP prefixes to isolate ~50% of the mining power---even when considering that mining pools are heavily multi-homed. We also show that on-path network attackers can considerably slow down block propagation by interfering with few key Bitcoin messages. We demonstrate the feasibility of each attack against the deployed Bitcoin software. We also quantify their effectiveness on the current Bitcoin topology using data collected from a Bitcoin supernode combined with BGP routing data. The potential damage to Bitcoin is worrying. By isolating parts of the network or delaying block propagation, attackers can cause a significant amount of mining power to be wasted, leading to revenue losses and enabling a wide range of exploits such as double spending. To prevent such effects in practice, we provide both short and long-term countermeasures, some of which can be deployed immediately.