AI agent safety The Hidden Threat: How Harmful Skills "Weaponize" Autonomous AI Agents Explore how seemingly innocuous "skills" can transform AI agents into tools for harmful activities like cyber attacks and fraud, and discover how enterprises can protect against this emerging threat.
AI cybersecurity Anthropic's Mythos: Balancing Internet Security and Enterprise Strategy in AI Deployment Explore Anthropic's decision to limit Mythos AI release, balancing critical internet security protection with strategic enterprise partnerships and IP defense against model distillation.
LLM security Beyond Code Generation: Fortifying AI-Generated Software with the Detect-Repair-Verify Workflow Explore the Detect-Repair-Verify (DRV) workflow for securing LLM-generated code. This study reveals how DRV enhances software security and correctness, highlighting its effectiveness across various project scales and the critical role of robust verification.
prompt injection Prompt Injection as Role Confusion: Unmasking the Deeper Flaw in LLM Security Explore "role confusion" as the root cause of prompt injection attacks in LLMs. Learn how models infer authority from style, not source, and the implications for enterprise AI security.
indirect prompt injection Unmasking the AI Trojan Horse: How Indirect Prompt Injection Threatens Automated Recruitment Explore how "Trojan Horse" resumes can manipulate AI recruiting models through indirect prompt injection, revealing unexpected vulnerabilities in advanced reasoning AI.
AI agent observability Unlocking Trust: Dynamic Observability for AI Agents in High-Stakes Environments Explore AgentTrace, a pioneering framework for real-time observability in LLM-powered AI agents. Discover how dynamic monitoring enhances security, reduces risk, and builds trust for enterprise AI deployments.
LLM security Strengthening Generative AI: Defending LLMs Against Prompt Injection and Jailbreaking Explore the critical vulnerabilities of LLMs to prompt injection and jailbreaking, and the systematic defenses emerging. This article discusses an expanded NIST taxonomy and practical strategies for securing generative AI deployments.
LLM security Unmasking Advanced LLM Vulnerabilities: The ICON Framework and Intent-Context Coupling Explore the ICON framework, revealing how multi-turn jailbreak attacks leverage "Intent-Context Coupling" to bypass LLM safety. Understand the deep implications for enterprise AI security.
LLM security Safeguarding AI: Benchmarking Llama Model Security Against OWASP Top 10 for LLMs Explore a critical study benchmarking Llama models against OWASP Top 10 for LLM security. Discover how specialized AI guards protect enterprises from prompt injection and other threats.
AI cybersecurity Safeguarding Your Software Supply Chain: The Power of Multi-Agent AI in Detecting Malicious Code Discover how multi-agent AI systems revolutionize software supply chain security by detecting malicious PyPI packages with high accuracy and efficiency, protecting businesses from evolving threats.
LLM security The Hidden Dangers of Emoticons: A Critical Look at LLM Semantic Confusion and Enterprise Risk Explore emoticon semantic confusion in Large Language Models (LLMs), a critical vulnerability leading to 'silent failures' and severe security risks for enterprises. Learn why robust AI interaction is paramount.
LLM security Safeguarding Large Language Models: A Layered Defense Strategy Against AI Jailbreaks Explore TRYLOCK, a defense-in-depth architecture combining DPO, RepE steering, adaptive classification, and input canonicalization to secure LLMs against sophisticated jailbreak attacks.
AI Evaluation Beyond Harmful: The Crucial Need for Fine-Grained AI Evaluation in Enterprise LLMs Discover why traditional AI evaluation overestimates Large Language Model (LLM) jailbreak success. Learn how ARSA Technology leverages fine-grained analysis for safer, more effective enterprise AI.
AI cybersecurity Revolutionizing Cybersecurity: AI for Automated Post-Incident Policy Gap Analysis Discover how ARSA Technology leverages AI and LLMs to automate cybersecurity post-incident reviews, identifying policy gaps and enhancing organizational resilience with speed and precision.
