Flexible Hardware-Enabled Guarantees for AI Compute

As artificial intelligence systems become increasingly powerful, they pose growing risks to international security, creating urgent coordination challenges that current governance approaches struggle to address without compromising sensitive information or national security. We propose flexible...

FORTRESS: Frontier Risk Evaluation for National Security and Public Safety

The rapid advancement of large language models LLMs introduces dual-use capabilities that could both threaten and bolster national security and public safety NSPS. Models implement safeguards to protect against potential misuse relevant to NSPS and allow for benign users to receive helpful...

Position: Certified Robustness Does Not (Yet) Imply Model Security

While certified robustness is widely promoted as a solution to adversarial examples in Artificial Intelligence systems, significant challenges remain before these techniques can be meaningfully deployed in real-world applications. We identify critical gaps in current research, including the parad...

ExtendAttack: Attacking Servers of LRMs via Extending Reasoning

Large Reasoning Models LRMs have demonstrated promising performance in complex tasks. However, the resource-consuming reasoning processes may be exploited by attackers to maliciously occupy the resources of the servers, leading to a crash, like the DDoS attack in cyber. To this end, we propose a...

Evaluation Pipeline for Systematically Searching for Anomaly Detection Systems

Digitalization in the medical world provides major benefits while making it a target for attackers and thus hard to secure. To deal with network intruders we propose an anomaly detection system on hardware to detect malicious clients in real-time. We meet real-time and power restrictions using...

Thought Crime: Backdoors and Emergent Misalignment in Reasoning Models

Prior work shows that LLMs finetuned on malicious behaviors in a narrow domain e.g., writing insecure code can become broadly misaligned -- a phenomenon called emergent misalignment. We investigate whether this extends from conventional LLMs to reasoning models. We finetune reasoning models on...

From LLMs to MLLMs to Agents: a Survey of Emerging Paradigms in Jailbreak Attacks and Defenses within LLM Ecosystem

Large language models LLMs are rapidly evolving from single-modal systems to multimodal LLMs and intelligent agents, significantly expanding their capabilities while introducing increasingly severe security risks. This paper presents a systematic survey of the growing complexity of jailbreak...

Using LLMs for Security Advisory Investigations: How Far Are We?

Large Language Models LLMs are increasingly used in software security, but their trustworthiness in generating accurate vulnerability advisories remains uncertain. This study investigates the ability of ChatGPT to 1 generate plausible security advisories from CVE-IDs, 2 differentiate real from fa...

Arbitrary Code Injection

Overview org.webjars.npm:jsonpath is a Query JavaScript objects with JSONPath expressions. Robust / safe JSONPath engine for Node.js. Affected versions of this package are vulnerable to Arbitrary Code Injection via unsafe evaluation of user-supplied JSON Path expressions. The library relies on th...

Arbitrary Code Injection

Overview jsonpath is a Query JavaScript objects with JSONPath expressions. Robust / safe JSONPath engine for Node.js. Affected versions of this package are vulnerable to Arbitrary Code Injection via unsafe evaluation of user-supplied JSON Path expressions. The library relies on the static-eval...

A Smart Contract-Based Non-Transferable Signature Verification System Using Nominative Signatures

Nominative signatures allow us to indicate who can verify a signature, and they can be employed to construct a non-transferable signature verification system that prevents the signature verification by a third party in unexpected situations. For example, this system can prevent IOU/loan certifica...

SafeGenBench: a Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code

The code generation capabilities of large language modelsLLMs have emerged as a critical dimension in evaluating their overall performance. However, prior research has largely overlooked the security risks inherent in the generated code. In this work, we introduce SafeGenBench, a benchmark...

SAFER-D: a Self-Adaptive Security Framework for Distributed Computing Architectures

The rise of the Internet of Things and Cyber-Physical Systems has introduced new challenges on ensuring secure and robust communication. The growing number of connected devices increases network complexity, leading to higher latency and traffic. Distributed computing architectures DCAs have gaine...

The Hitchhiker'S Guide to Efficient, End-To-End, and Tight DP Auditing

This paper systematizes research on auditing Differential Privacy DP techniques, aiming to identify key insights into the current state of the art and open challenges. First, we introduce a comprehensive framework for reviewing work in the field and establish three cross-contextual desiderata tha...

Rubber Mallet: a Study of High Frequency Localized Bit Flips and Their Impact on Security

The increasing density of modern DRAM has heightened its vulnerability to Rowhammer attacks, which induce bit flips by repeatedly accessing specific memory rows. This paper presents an analysis of bit flip patterns generated by advanced Rowhammer techniques that bypass existing hardware defenses...

LS Electric GMWin 4

RISK EVALUATION Successful exploitation of these vulnerabilities could allow an attacker to disclose information or execute arbitrary code. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities, such as: Minimize...

Eval Injection

Overview letta is a Create LLM agents with long-term memory and custom tools Affected versions of this package are vulnerable to Eval Injection via the functionmessage process. An attacker can execute arbitrary code by manipulating the functionname or functionargs arguments. Remediation There is ...

CVE-2025-6101 letta-ai letta interface.py function_message eval injection

A vulnerability classified as critical has been found in letta-ai letta up to 0.4.1. Affected is the function functionmessage of the file letta/letta/interface.py. The manipulation of the argument functionname/functionargs leads to improper neutralization of directives in dynamically evaluated...

letta-ai letta 安全漏洞

Letta-ai letta is a stateful agent framework with memory, inference, and context management in the Letta-ai open source. A security vulnerability exists in letta-ai letta version 0.4.1 and earlier, which stems from a dynamic code evaluation malpractice issue that could lead to the execution of...

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications

Recent advancements in LLMs indicate potential for novel applications, e.g., through reasoning capabilities in the latest OpenAI and DeepSeek models. For applying these models in specific domains beyond text generation, LLM-based multi-agent approaches can be utilized that solve complex tasks by...