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Stealing Reasoning Traces from Proprietary LLM APIs [84.58379404903394]
We develop a scalable decryption jailbreak for encrypted reasoning traces.<n>It circumvents anti-distillation mechanisms, allowing adversaries to extract a proprietary model's reasoning.<n>It inadvertently reveals hazardous information hidden within the reasoning process.
arXiv Detail & Related papers (2026-08-10T17:24:50Z)
Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens [65.53494487819053]
Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services.<n>We adopt a computer-system analogy: treating a Claw-like agent as an agentic computer system whose gateway runtime plays an OS-like mediation role.<n>We develop SafeClawArena, a benchmark of 406 adversarial tasks across four attack surfaces.
arXiv Detail & Related papers (2026-06-29T18:00:45Z)
DKVE: Decentralized Key Validation for End-to-End Encrypted Messaging [0.7978226788634308]
End-to-end encrypted messaging systems depend on authentic public key distribution to prevent man-in-the-middle (MitM) attacks.<n>Current solutions present a stark trade-off: out-of-band (OOB) verification provides strong security but lacks scalability for large contact lists.<n>We propose DKVE, a protocol that validates public keys through privacy-preserving cross-validation within users' social graphs.
arXiv Detail & Related papers (2026-06-25T00:43:59Z)
CapSeal: Capability-Sealed Secret Mediation for Secure Agent Execution [1.9473631530390483]
CapSeal is a capability-sealed secret mediation architecture that replaces direct secret access with constrained invocations through a local trusted broker.<n>We describe a Rust prototype integrated with an MCP-facing adapter, formulate conditional security goals for non-disclosure, constrained use, replay resistance, and auditability.
arXiv Detail & Related papers (2026-04-18T00:23:32Z)
SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization [50.71047638695205]
Reasoning language models (RLMs) are increasingly used in programming.<n>Yet, even state-of-the-art RLMs frequently introduce critical security vulnerabilities in generated code.<n>We present SecPI, a fine-tuning pipeline that teaches RLMs to internalize structured security reasoning.
arXiv Detail & Related papers (2026-04-04T04:29:11Z)
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Federated Learning (FL) enables collaborative training while keeping sensitive data on clients' devices, but local model updates can still leak private information.<n> Hybrid Homomorphic Encryption (HHE) has recently been applied to FL to mitigate client overhead while preserving privacy.<n>We integrate two alternative key protection mechanisms into the HHE-FL workflow.
arXiv Detail & Related papers (2026-03-27T13:46:29Z)
Building a Robust Risk-Based Access Control System to Combat Ransomware's Capability to Encrypt: A Machine Learning Approach [0.510691253204425]
Ransomware core capability, unauthorized encryption, demands controls that identify and block malicious cryptographic activity without disrupting legitimate use.<n>We present a probabilistic, risk-based access control architecture that couples machine learning inference with mandatory access control to regulate encryption on Linux in real time.
arXiv Detail & Related papers (2026-01-23T14:48:35Z)
The Trojan Knowledge: Bypassing Commercial LLM Guardrails via Harmless Prompt Weaving and Adaptive Tree Search [58.8834056209347]
Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs.<n>We introduce the Correlated Knowledge Attack Agent (CKA-Agent), a dynamic framework that reframes jailbreaking as an adaptive, tree-structured exploration of the target model's knowledge base.
arXiv Detail & Related papers (2025-12-01T07:05:23Z)
Mind Your Server: A Systematic Study of Parasitic Toolchain Attacks on the MCP Ecosystem [13.95558554298296]
Large language models (LLMs) are increasingly integrated with external systems through the Model Context Protocol (MCP)<n>In this paper, we reveal a new class of attacks, Parasitic Toolchain Attacks, instantiated as MCP Unintended Privacy Disclosure (MCP-UPD)<n>The malicious logic infiltrates the toolchain and unfolds in three phases: Parasitic Ingestion, Privacy Collection, and Privacy Disclosure, culminating in stealthy exfiltration of private data.
arXiv Detail & Related papers (2025-09-08T11:35:32Z)
KubeGuard: LLM-Assisted Kubernetes Hardening via Configuration Files and Runtime Logs Analysis [21.638320707123444]
KubeGuard is a novel runtime log-driven recommender framework for cluster security.<n>It is designed to harden K8s environments through two complementary tasks: Resource Creation and Resource Refinement.<n>Our evaluation demonstrates that KubeGuard effectively generates and refines K8s manifests for Roles, NetworkPolicies, and Deployments.
arXiv Detail & Related papers (2025-09-04T13:13:57Z)
Cryptanalysis of LC-MUME: A Lightweight Certificateless Multi-User Matchmaking Encryption for Mobile Devices [0.0]
We show that a Type-I adversary can successfully forge a validtext cipher without possessing the complete private key of the sender.<n>We propose a strategy to strengthen the security of matchmaking encryption schemes in mobile computing environments.
arXiv Detail & Related papers (2025-07-30T13:36:52Z)
Breaking the Bulkhead: Demystifying Cross-Namespace Reference Vulnerabilities in Kubernetes Operators [3.1176172329546894]
This paper is the first to systematically investigate the security vulnerability of Operators.<n>We found that over 14% of Operators in the wild are potentially vulnerable.<n>Our findings have been reported to the relevant developers.
arXiv Detail & Related papers (2025-07-04T08:44:24Z)
Striking Back At Cobalt: Using Network Traffic Metadata To Detect Cobalt Strike Masquerading Command and Control Channels [0.22499166814992436]
Off-the-shelf software for Command and Control is often used by attackers and legitimate pentesters.<n>Cobalt Strike is one of the most famous solutions in this category, used by known advanced attacker groups such as "Mustang Panda" or "Nobelium"
arXiv Detail & Related papers (2025-06-10T15:47:22Z)
KubeFence: Security Hardening of the Kubernetes Attack Surface [3.67004863266419]
K8s is widely used to orchestrate containerized applications, including critical services in domains such as finance, healthcare, and government.<n>Its extensive and feature-rich API interface exposes a broad attack surface, making K8s vulnerable to exploits of software vulnerabilities and misconfigurations.<n>This paper proposes a novel solution, KubeFence, which implements finer-grain API filtering tailored to specific client workloads.
arXiv Detail & Related papers (2025-04-15T12:15:34Z)
Defeating Prompt Injections by Design [79.00910871948787]
CaMeL is a robust defense that creates a protective system layer around the Large Language Models.<n>To operate, CaMeL explicitly extracts the control and data flows from the (trusted) query.<n>To further improve security, CaMeL uses a notion of a capability to prevent the exfiltration of private data over unauthorized data flows.
arXiv Detail & Related papers (2025-03-24T15:54:10Z)
Privacy-preserving server-supported decryption [2.2530496464901106]
We consider encryption systems with two-out-of-two threshold decryption, where one of the parties initiates the decryption and the other one assists. Existing threshold decryption schemes disclose to the server the ciphertext that is being decrypted. We give a construction, where the identity of the ciphertext is not leaked to the server, and the client's privacy is preserved.
arXiv Detail & Related papers (2024-10-25T06:47:53Z)
CCA-Secure Key-Aggregate Proxy Re-Encryption for Secure Cloud Storage [1.4610685586329806]
Data protection in cloud storage is the key to the survival of the cloud industry. Proxy Re-Encryption schemes enable users to convert their ciphertext into others ciphertext by using a re-encryption key. Recently, we lowered the key storage cost of C-PREs to constant size and proposed the first Key-Aggregate Proxy Re-Encryption scheme.
arXiv Detail & Related papers (2024-10-10T17:02:49Z)
ASPIRER: Bypassing System Prompts With Permutation-based Backdoors in LLMs [17.853862145962292]
We introduce a novel backdoor attack that systematically bypasses system prompts. Our method achieves an attack success rate (ASR) of up to 99.50% while maintaining a clean accuracy (CACC) of 98.58%.
arXiv Detail & Related papers (2024-10-05T02:58:20Z)
Implementation of New Security Features in CMSWEB Kubernetes Cluster at CERN [1.6804702845109005]
We discuss new security features introduced to the CMSWEB ("k8s") cluster. The new features include the implementation of network policies, deployment of Open Policy Agent (OPA), enforcement of OPA policies, and the integration of Vault.
arXiv Detail & Related papers (2024-05-24T08:22:22Z)
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)
CodeChameleon: Personalized Encryption Framework for Jailbreaking Large Language Models [49.60006012946767]
We propose CodeChameleon, a novel jailbreak framework based on personalized encryption tactics. We conduct extensive experiments on 7 Large Language Models, achieving state-of-the-art average Attack Success Rate (ASR) Remarkably, our method achieves an 86.6% ASR on GPT-4-1106.
arXiv Detail & Related papers (2024-02-26T16:35:59Z)
Instructional Fingerprinting of Large Language Models [57.72356846657551]
We present a pilot study on fingerprinting Large language models (LLMs) as a form of very lightweight instruction tuning. Results on 11 popularly-used LLMs showed that this approach is lightweight and does not affect the normal behavior of the model. It also prevents publisher overclaim, maintains robustness against fingerprint guessing and parameter-efficient training, and supports multi-stage fingerprinting akin to MIT License.
arXiv Detail & Related papers (2024-01-21T09:51:45Z)
SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
We propose SOCI+ which significantly improves the performance of SOCI. SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive. Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
arXiv Detail & Related papers (2023-09-27T05:19:32Z)
Exploiting Logic Locking for a Neural Trojan Attack on Machine Learning Accelerators [4.605674633999923]
We show how logic locking can be used to compromise the security of a neural accelerator it protects. Specifically, we show how the deterministic errors caused by incorrect keys can be harnessed to produce neural-trojan-style backdoors.
arXiv Detail & Related papers (2023-04-12T17:55:34Z)
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning [63.45532264721498]
Self-supervised learning is an emerging technique to pre-train encoders using unlabeled data. We perform first systematic, principled measurement study to understand whether and when a pretrained encoder can address the limitations of secure or privacy-preserving supervised learning algorithms.
arXiv Detail & Related papers (2022-12-06T21:35:35Z)
Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
arXiv Detail & Related papers (2021-06-03T16:45:40Z)

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