Search arXivSearch

arXiv · 2607.07109

Certifying Ghosts: How Cybersecurity AI Agents Break the EU Cyber Resilience Act

Abstract

The EU Cyber Resilience Act (CRA) makes a smart bet. It does not demand that products be free of vulnerabilities, but only that manufacturers run a process: assess risk, handle flaws, ship updates. The bet pays off if four things about the world stay true: (P1) finding vulnerabilities is slow, skilled, human work; (P2) a product's exploitable flaws are knowable the day it ships; (P3) exploitation is rare enough to notice; and (P4) fixes keep pace with discovery. Cybersecurity AI (CAI) agents, AI put to work finding and exploiting flaws in other products, falsify all four. The regime answers in two opposite ways. Against the sheer volume of flaws that agents surface it bends (P1): built for scarce attention, it re-centres compliance on defensible, documented prioritisation, and holds. But agents also collapse the speed and economics of the vulnerability lifecycle, and here it breaks (P2, P3, P4): a product that passed every check becomes exploitable without anyone touching it, so its market-entry test, its reporting trigger, and its one-and-done certificate vouch for a security that has quietly expired. The fault is in the landscape, not the product, so running the process more diligently cannot repair it. We map each mechanism to the force that strains or snaps it, and find the cure and the disease cut from the same cloth: because defenders and attackers wield the same AI, the only conformity that survives is one that never stops running. We also carry the remedy from proposal to proof on two CRA-scope robots, a humanoid and a lawn mower, where an agentic defender holds a line their undefended selves cannot. On the evidence already in hand, the CRA reaches full force in December 2027 certifying products against a world that has already changed. Static, human-paced security is finished; what replaces it must be continuous and agent-operated, and that is no longer a matter of taste.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Víctor Mayoral-Vilches. 2026-07-08. Certifying Ghosts: How Cybersecurity AI Agents Break the EU Cyber Resilience Act. https://arxiv.org/abs/2607.07109

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Starfish: Rebalancing Multi-Party Off-Chain Payment Channels

Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network (PCN), as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channel, restricting the ability of PCNs to transact through a path and subsequently reducing the transaction success rate. To address this issue, the technology of rebalancing has been proposed. However, existing rebalancing strategies in PCNs are limited in their efficiency and applicability under realistic network topologies. Cycle-based approaches only enable rebalancing among nodes that form cycles, while non-cycle-based approaches incur high on-chain operation costs. In this study, we propose Starfish, a rebalancing approach designed for the locally star-like structures formed by high-degree routing hubs and their incident channels in real-world PCNs. By leveraging a hub-centric structure, Starfish reduces the on-chain overhead of rebalancing from quadratic to linear in the number of channels, achieving high rebalancing efficiency. We formally prove the security of Starfish and conduct comparative experiments against existing rebalancing techniques to demonstrate its effectiveness.

cs.CR

Proof-of-Authorship for Diffusion-based AI Generated Content

Recent advancements in AI-generated content (AIGC) have introduced new challenges in intellectual property protection and the authentication of generated objects. We focus on scenarios in which an author seeks to assert authorship of an object generated using latent diffusion models (LDMs), in the presence of adversaries who attempt to falsely claim authorship of objects they did not create. While proof-of-ownership has been studied in the context of multimedia content through techniques such as time-stamping and watermarking, these approaches face notable limitations. In contrast to traditional content creation sources (e.g., cameras), the LDM generation process offers greater control to the author. Specifically, the random seed used during generation can be deliberately chosen. By binding the seed to the author's identity using cryptographic functions, the author can assert to be the creator of the object. We refer to this stronger guarantee as proof-of-authorship, since only the creator of the object can legitimately claim the object. This contrasts with proof-of-ownership via time-stamping or watermarking, where any entity could potentially claim ownership of an object by being the first to timestamp or embed the watermark. We propose a proof-of-authorship framework involving a probabilistic adjudicator who quantifies the probability that a claim is false. Furthermore, unlike prior approaches, the proposed framework does not involve any secret. We explore various attack scenarios and analyze design choices using Stable Diffusion 2.1 and XL as representative case studies.

cs.CR

Impact Analysis of Speech Representation Learning Models for Acoustic Side-Channel Attack

Acoustic side-channel attacks (ASCA) on keyboards have gained increasing attention, yet impact of speech representation learning models in ASCA remains unexplored. Addressing this, we introduce KEYAC, a dataset designed to analyze representation generalization for ASCA under both standard and VoIP codec settings. On KEYAC, we evaluate six representation learning models under zero-shot and partial fine-tuning settings using fully connected and convolutional networks. Results show that while partial fine-tuning improves performance, models struggle to generalize across VoIP codecs. We hypothesize this limitation stems from inadequate modeling of nonlinear feature interactions in conventional fine-tuning architectures. To address this, we employ Kolmogorov-Arnold Networks (KAN) for fine-tuning. Empirical results show that KAN-based fine-tuning consistently outperforms the baselines and establishes a new state-of-the-art on KEYAC.

cs.CR