DSPrompt: Dynamic Soft Prompt Defense against M-RAG Corruption

Multimodal Retrieval Augmented Generation M-RAG is increasingly vulnerable to adversarial attacks where malicious data are crafted to produce embeddings that align with benign entries in the vector space, deceiving retrieval and inducing harmful outputs. Existing defenses primarily operate at que...

Breaking and Defending LLM-Powered Social Media Bot Detection Systems

The rise of social media bots poses a persistent threat, enabling misinformation, opinion manipulation, and the erosion of trust in online platforms. To combat this, machine learning systems have been developed to detect and limit bot activity, but attackers continuously adapt through techniques...

Adversarial Robustness in Smishing Detection: A Comparative Analysis of Adversarial Fragility in Classical Vs. Transformer-Based Detection Systems

Smishing detection systems are commonly trained and evaluated on clean, monolingual text. In low-resource settings, however, attackers frequently circumvent these systems through character obfuscation, cross-lingual code-switching, and structural perturbation. This study evaluates adversarial...

ToolHazard: Scaling Adversarial Environments for Security Evaluation and Alignment of LLM-Based Agents

Large language model LLM agents integrated with external tools are vulnerable to indirect prompt injections embedded in environmental states. However, existing studies largely rely on manually implemented or reused environments, stochastic LLM-based tool simulation, and predefined injection...

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models

Large Language Models LLMs have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning, tool invocation, code execution, and maintaining persistent memory. When these agents operate with real-world privileges---calling APIs, modifying files,...

Generating Attacks for LLMs with GFlowNets

The rapid advancement of Large Language Models LLMs has facilitated their ubiquitous integration into various domains, leading to widespread adoption. However, this escalating trend has introduced significant security vulnerabilities, necessitating the identification and mitigation of flaws arisi...

Never Stop Speaking: A Denial-Of-Service Attack on End-To-End Speech Language Models

Many studies have shown that specially crafted inputs can induce large language models LLMs to generate excessively long outputs, resulting in significant computational overhead and resource consumption. While most existing denial-of-service DoS attacks target text-only LLMs, end-to-end E2E speec...

Telemetry and Concealment in Self-Adapting Generative AI: Logging Architecture, Adversarial Model Hiding, and the Limits of Detection

Model risk management MRM guidance assumes a static model lifecycle, in which models are developed, independently validated, and implemented without further autonomous modification. Continually self-adapting generative AI systems --- models that update their own weights during production deployme...

Adversarial Clothing Designed to Fool Facial Recognition Systems

There are many companies manufacturing adversarial clothing designed to confuse facial recognition systems. It's a cool idea, but I worry that it's mostly security theater: "Our patterns play with that chaos, confuse algorithms and make it way harder to pin you down," he said. Bell, however, said...

augustus v0.14.15

Augustus - LLM vulnerability scanner for prompt injection, jailbreak, and adversarial attack testing Augustus - LLM Vulnerability Scanner Test large language models against 210+ adversarial attacks covering prompt injection, jailbreaks, encoding exploits, and data extraction. Augustus is a Go-bas...

Zero-Trust Federated Learning for Connected Aftermarket Devices

Connected aftermarket devices extend vehicle diagnostics, repair workflows, and over-the-air software maintenance beyond original equipment manufacturer boundaries, yet their heterogeneous ownership and long service life complicate conventional perimeter security. This paper develops Zero Trust...

When Efficiency Becomes Fragility: Exploiting Dynamic Routing Vulnerabilities in Adaptive UAV Tracking

Resource constraints on UAV platforms have driven a paradigm shift in aerial tracking, from pursuing performance toward balancing accuracy with efficiency. Adaptive Transformer Trackers, which leverage an input-dependent dynamic routing architecture, have emerged as a representative solution to...

Invisible Ink Threats: Adversarial Goals behind Legitimate Tasks in Computer-Use Agents

Computer-use agents CUAs, which empower large language models to autonomously operate operating systems and the web, are increasingly vulnerable to indirect prompt injection attacks. A widely adopted defense is the human-in-the-loop paradigm, in which the agent pauses for explicit user confirmati...

augustus v0.14.13

Augustus - LLM vulnerability scanner for prompt injection, jailbreak, and adversarial attack testing Augustus - LLM Vulnerability Scanner Test large language models against 210+ adversarial attacks covering prompt injection, jailbreaks, encoding exploits, and data extraction. Augustus is a Go-bas...

Adversarial Attacks in Multi-Agent LLM Pipelines: Unveiling Structural Vulnerabilities in Agentic AI Architectures

Multi-agent LLM pipelines orchestrate multiple specialized language model agents into structured workflows where intermediate outputs are passed across agents to solve complex tasks. This design introduces a security gap absent in single-agent settings: once an agent accepts adversarial content, ...

Skillsets on the Chain: A Blockchain-Based Zero-Trust Framework for Agentic AI Networking

Agentic AI networking AgentNet systems rely heavily on third-party skillset implementations and distributed multi-agent collaboration, yet they face major claim-to-capability inconsistencies and security vulnerabilities under trust-by-declaration assumptions. To bridge this gap, this paper propos...

adversarial-insight-ml (0.1.0), aiorobonect (>=0.2.6, <=0.2.8) +20 more potentially affected by CVE-2026-13346 via pip (23.2.1)

pip PYPI version 23.2.1 is affected by a known vulnerability. The following packages have a transitive dependency on pip and may be impacted: - adversarial-insight-ml 0.1.0 - aiorobonect =0.2.6, =2.0.3, =1.2.0, =1.0.0, =4.2.0, =0.0.2, =0.0.2, =0.0.5 - musclex 1.23.0 and more Source cves:...

augustus v0.14.12

Augustus - LLM vulnerability scanner for prompt injection, jailbreak, and adversarial attack testing Augustus - LLM Vulnerability Scanner Test large language models against 210+ adversarial attacks covering prompt injection, jailbreaks, encoding exploits, and data extraction. Augustus is a Go-bas...

A Structuration Approach to Theorizing Cybersecurity Practice: The STARC Model

The problem: Cybersecurity practice runs simultaneously across analysts, teams, organizations, sectors, and regulators, co-evolves with adversaries, and increasingly blends human and algorithmic decision-making. The theories applied to it operate at single organizational levels and cannot explain...

augustus v0.14.11

Augustus - LLM vulnerability scanner for prompt injection, jailbreak, and adversarial attack testing Augustus - LLM Vulnerability Scanner Test large language models against 210+ adversarial attacks covering prompt injection, jailbreaks, encoding exploits, and data extraction. Augustus is a Go-bas...