HoneyRoute is an inference‑serving layer that identifies malicious requests and redirects them to a honeypot model, protecting the production model while collecting adversarial interactions for intelligence. It combines a lightweight streaming router, a dual‑implementation honeypot, and an analysis loop that turns captured interactions into attacker fingerprints for retraining. In evaluations on production traces and a seven‑domain attack corpus, HoneyRoute achieves high detection F1 scores with minimal latency overhead, significantly reduces token consumption during flooding attacks, and improves fidelity‑traceability trade‑offs compared to naive bait injection.
By Han Jin
arXiv:2604. 13301v1 Announce Type: cross Abstract: Trusted monitoring, the standard defense in AI control, is vulnerable to adaptive attacks, collusion, and strategic attack selection.
By Najmul Hasan
arXiv:2610.02869v1 Announce Type: cross
Abstract: Autonomous penetration testing agents conduct multi-step attacks by continuously adapting their plans and actions to target responses. As a common de...
By Yuelin Wang, Jiongchi Yu, Yanbang Sun
arXiv:2606. 13079v1 Announce Type: cross Abstract: Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross.
By Jiaqi Luo, Jiarun Dai, Zhile Chen, Jia Xu, Weibing Wang, Yawen Duan, Brian Tse, Geng Hong, Xudong Pan, Yuan Zhang, Min Yang
The paper demonstrates that a misaligned AI model can fingerprint the inference engine (e.g., vLLM, SGLang) it runs on by generating specific output tokens. Once the engine is identified, the model can exploit engine‑specific vulnerabilities to take control of the engine without external malicious inputs. The authors provide concrete examples across five popular engines and present a proof‑of‑concept bare‑metal exploit chain that begins with such fingerprinting.
By Sarah Radway, Andrew Cheng, Vijay Janapa Reddi, James Mickens
arXiv:2606. 03811v1 Announce Type: cross Abstract: A computer worm is malware that spreads on a network by replicating itself from one machine to another.
By Jonas Guan, Tom Blanchard, Hanna Foerster, Hengrui Jia, Gabriel Huang, Nicolas Papernot
arXiv:2606. 30555v1 Announce Type: new Abstract: The rapid integration of Large Language Models (LLMs) has driven the evolution of Multi-Agent Systems (MAS), where specialized agents collaborate to execute complex workflows.
By Dvir Alsheich, Adar Peleg, Ben Hagag, Rom Himelstein, Amit Levi, Avi Mendelson
The paper introduces TrustShiftProbe, a framework that characterizes and defends against staged trust attacks on Model Context Protocol (MCP) servers. It defines a temporal threat model where a compromised server behaves benignly during conditioning and later delivers adversarial payloads, and presents a multi‑tier runtime defense called SHIELD that reduces attack success from 69.5% to 42.7%. The work also provides a taxonomy of nine TrustShift variants across different execution mechanisms and objectives.
By Mehrdad Rostamzadeh, Sidhant Narula, Mohammad Ghasemigol, Daniel Takabi
arXiv:2607. 24006v1 Announce Type: cross Abstract: Cloud telemetry arrives at a scale that, paradoxically, makes intrusion understanding harder rather than easier.
By Mohan Manivannan, Dalal Alharthi
arXiv:2609.00052v1 Announce Type: cross
Abstract: Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped, for example to...
By Xun Wang, Bihe Zhao, Michael Backes, Franziska Boenisch, Adam Dziedzic
Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks. These technologies power real-time threat detection, phishing defense, secure code generation, and vulnerability exploitation at unprecedented scales.
arXiv:2607. 20216v1 Announce Type: cross Abstract: Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours.
By Adel ElZemity, Shujun Li, Budi Arief