Benchmarking Identity-Sensitive LLM Outputs for Surveillance and Security Robots

Large language models LLMs are increasingly used to generate textual robot design specifications, interaction policies, and risk assessments during early-stage robot development. Such outputs may influence how surveillance and security robots are conceptualized, documented, and ultimately...

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...

Hierarchical Agentic Incident Response with Digital-Twin-Validated Attack Inference

Network incident response remains slow and labor-intensive as the defender must infer multi-stage attacks from partial observations and translate recovery decisions into reliable system commands. Decision-theoretic planners provide principled optimization but typically rely on abstract states and...

Beyond Direct Access: Resource Hijacking in LLM Agents

Large language model agents are increasingly connected to high-value resources such as computing infrastructure, credentials, usage budgets, identities, private knowledge, communication channels, and organizational workflows. Existing agent security research mainly studies attacks on instructions...

WeSCE: A Benchmark for Measuring Security Drift in LLM-Driven Code Editing

In this work, we introduce WeSCE, a benchmark for quantifying security drift in code editing under weak-security constraints, where tasks specify only functional objectives without explicit security requirements. WeSCE consists of 400 executable programs derived from real-world code, covering...

cynative v1.9.2

Build your own security agents Open-source framework for security agents with live, read-only access to your infrastructure. Quickstart · Your first agent · Docs Ask your infrastructure anything. Cynative runs frontier models across your code, cloud and runtime - reasoning through GitHub, GitLab,...

Ai-Vulnerability-Code-Detector

AI-Powered Vulnerability Scanner A comprehensive security sca...

The OWASP Top 10 for LLM Applications 2026: From Model Risks to Agentic Security

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MazeRunner: Nonlinear Task and Clue Orchestration for LLM-Driven Black-Box Automated Penetration Testing

Penetration testing is essential yet resource-intensive. Although large language models LLMs show promise for automating security auditing, existing agents mainly execute end-to-end workflows in simplified linear scenarios. Real-world black-box testing is fundamentally nonlinear: the attack graph...

A Hybrid LLM-Based Framework for Automated Security Annotation Generation in Business Process Models

The modelling and analysis of secure business processes require the incorporation of security annotations into process models. Although BPMN extensions, including SecBPMN2, exist for this purpose, the derivation of accurate and complete security annotations from natural-language specifications...

Extracting and Verifying Illicit Bitcoin Addresses from Underground Forum Discussions

Existing labeled Bitcoin datasets are largely derived from community-reported abuse, blockchain heuristics, incident-specific collections, or proprietary labeling processes. Their construction methods are rarely publicly reproducible and often provide limited evidence that an address was directly...

CyberStrike v1.1.16

English | 简体中文 | 繁體中文 | 한국어 | Deutsch | Español | Français | Italiano | Dansk | 日本語 | Polski | Русский | Bosanski | العربية | Norsk | Português Brasil | ไทย | Türkçe | Українська | বাংলা | Ελληνικά | Tiếng Việt | हिन्दी The first open-source AI agent built for offensive security. Automated...

Does Fixing Break Security? an Empirical Study of Security Degradation in Iterative LLM-Driven Infrastructure-As-Code Repair

Background: Iterative feedback loops are the dominant paradigm for improving LLM-generated Infrastructure-as-Code IaC: validators such as Checkov and terraform validate feed error signals back for successive repair attempts. Prior work reports cumulative-best metrics, which are non-decreasing by...

LLM-Assisted Dynamic Threat Analysis for Attacker-Reachable Software Weaknesses in Autonomous Vehicles

Autonomous vehicles depend on large safety-critical software stacks, where weaknesses reachable from adversarial inputs may affect steering, braking, or other control decisions. Static analysis can identify candidate sites, but dynamically confirming exploitability requires executable test...

Beyond Handcrafted Security: Towards Self-Evolving Defense for LLM Agents

The expanding operational capabilities of large language model LLM agents introduce sophisticated security threats. Runtime defenses have emerged as an effective approach to mitigating these risks by integrating security mechanisms into the agent execution loop. However, existing runtime defenses...

CoSA: Context-Aware Severity Assessment Via Context Analysis with Large Language Models

Accurate vulnerability severity assessment is essential for prioritizing remediation, yet manually assessing Common Vulnerability Scoring System CVSS base metrics remains labor-intensive. Existing automated approaches often fail to capture the repository-level evidence required for assessing many...

Prompt Injections for Defense

This seems to work: Researchers from Tracebit on Monday said they found that placing prompt injections alongside passwords, cryptographic keys, and other secrets stored on Amazon Web Services was often all that was needed to shut down attacks from AI hacking agents. The prompts direct the attacki...

Black Hat Europe 2024 Conference Slides

Black Hat Europe 2024 conference slides for topics Vulnerabilities in the eSIM download protocol, WiFi Calling: Revealing Downgrade Attacks and Not-so-private private Keys, From Pass-the-Hash to Code Execution on Schneider Electric M340 PLCs, CodeCloak: A DRL-Based Method for Mitigating Code...

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...

CVE-2026-73032

PapersGPT for Zotero 0.6.1 contains a remote code execution vulnerability that allows attackers to execute arbitrary JavaScript by returning malicious code from an LLM endpoint that is passed unsanitized to window.eval in views.ts. Attackers can exploit this through prompt injection in PDFs, MITM...