Hedge Funds on a Swamp: Analyzing Patterns, Vulnerabilities, and Defense Measures in Blockchain Bridges [Experiment, Analysis and Benchmark]

Blockchain bridges have become essential infrastructure for enabling interoperability across different blockchain networks, with more than $24B monthly bridge transaction volume. However, their growing adoption has been accompanied by a disproportionate rise in security breaches, making them the...

Agent Safety Alignment Via Reinforcement Learning

The emergence of autonomous Large Language Model LLM agents capable of tool usage has introduced new safety risks that go beyond traditional conversational misuse. These agents, empowered to execute external functions, are vulnerable to both user-initiated threats e.g., adversarial prompts and...

TELSAFE: Security Gap Quantitative Risk Assessment Framework

Gaps between established security standards and their practical implementation have the potential to introduce vulnerabilities, possibly exposing them to security risks. To effectively address and mitigate these security and compliance challenges, security risk management strategies are essential...

A Survey on Artificial Noise for Physical Layer Security: Opportunities, Technologies, Guidelines, Advances, and Trends

Due to the broadcast nature of wireless communications, physical-layer security has attracted increasing concerns from both academia and industry. Artificial noise AN, as one of the promising physical-layer security techniques, is capable of utilizing the spatial degree-of-freedom of channels to...

Boosting Generative Adversarial Transferability with Self-Supervised Vision Transformer Features

The ability of deep neural networks DNNs come from extracting and interpreting features from the data provided. By exploiting intermediate features in DNNs instead of relying on hard labels, we craft adversarial perturbation that generalize more effectively, boosting black-box transferability...

Microsoft Named a Leader in the 2025 IDC CNAPP MarketScape: Key Takeaways for Security Buyers

The cloud-native application protection platform CNAPP market continues to evolve rapidly as organizations look to secure increasingly complex cloud environments. In the recently published 2025 IDC MarketScape for Worldwide CNAPP, Microsoft has been recognized as a Leader, reaffirming its...

SV-LLM: an Agentic Approach for SoC Security Verification Using Large Language Models

Ensuring the security of complex system-on-chips SoCs designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, comprehensiveness, and adaptability. The advent of large language models LLMs, with their...

DinoCompanion: an Attachment-Theory Informed Multimodal Robot for Emotionally Responsive Child-AI Interaction

Children's emotional development fundamentally relies on secure attachment relationships, yet current AI companions lack the theoretical foundation to provide developmentally appropriate emotional support. We introduce DinoCompanion, the first attachment-theory-grounded multimodal robot for...

GNSS Spoofing Detection Based on Opportunistic Position Information

The limited or no protection for civilian Global Navigation Satellite System GNSS signals makes spoofing attacks relatively easy. With modern mobile devices often featuring network interfaces, state-of-the-art signals of opportunity SOP schemes can provide accurate network positions in replacemen...

Agent Capability Negotiation and Binding Protocol (ACNBP)

As multi-agent systems evolve to encompass increasingly diverse and specialized agents, the challenge of enabling effective collaboration between heterogeneous agents has become paramount, with traditional agent communication protocols often assuming homogeneous environments or predefined...

PROVSYN: Synthesizing Provenance Graphs for Data Augmentation in Intrusion Detection Systems

Provenance graph analysis plays a vital role in intrusion detection, particularly against Advanced Persistent Threats APTs, by exposing complex attack patterns. While recent systems combine graph neural networks GNNs with natural language processing NLP to capture structural and semantic features...

Membership Inference Attacks on Sequence Models

Sequence models, such as Large Language Models LLMs and autoregressive image generators, have a tendency to memorize and inadvertently leak sensitive information. While this tendency has critical legal implications, existing tools are insufficient to audit the resulting risks. We hypothesize that...

Combining Threat Intelligence with IoT Scanning to Predict Cyber Attack

While the Web has become a global platform for communication, malicious actors, including hackers and hacktivist groups, often disseminate ideological content and coordinate activities through the "Dark Web", an obscure counterpart of the conventional web. Presently, challenges such as informatio...

Red-Teaming Text-To-Image Systems by Rule-Based Preference Modeling

Text-to-image T2I models raise ethical and safety concerns due to their potential to generate inappropriate or harmful images. Evaluating these models' security through red-teaming is vital, yet white-box approaches are limited by their need for internal access, complicating their use with...

Efficient and Stealthy Jailbreak Attacks Via Adversarial Prompt Distillation from LLMs to SLMs

Attacks on large language models LLMs in jailbreaking scenarios raise many security and ethical issues. Current jailbreak attack methods face problems such as low efficiency, high computational cost, and poor cross-model adaptability and versatility, which make it difficult to cope with the rapid...

LLM-Driven APT Detection for 6G Wireless Networks: a Systematic Review and Taxonomy

Sixth Generation 6G wireless networks, which are expected to be deployed in the 2030s, have already created great excitement in academia and the private sector with their extremely high communication speed and low latency rates. However, despite the ultra-low latency, high throughput, and...

Modeling Interdependent Privacy Threats

The rise of online social networks, user-gene-rated content, and third-party apps made data sharing an inevitable trend, driven by both user behavior and the commercial value of personal information. As service providers amass vast amounts of data, safeguarding individual privacy has become...

ACSE-Eval: Can LLMs Threat Model Real-World Cloud Infrastructure?

While Large Language Models have shown promise in cybersecurity applications, their effectiveness in identifying security threats within cloud deployments remains unexplored. This paper introduces AWS Cloud Security Engineering Eval, a novel dataset for evaluating LLMs cloud security threat...

Ghosted by a cybercriminal

Welcome to this week's edition of the Threat Source newsletter. Talos recently published research into how threat actors are increasingly teaming up across the attack chain. Each group handles a slice of the operation, passing the breach along like a relay baton. It's a concerning trend -- one th...

SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning

Large Reasoning Models LRMs introduce a new generation paradigm of explicitly reasoning before answering, leading to remarkable improvements in complex tasks. However, they pose great safety risks against harmful queries and adversarial attacks. While recent mainstream safety efforts on LRMs,...