LLMail-Inject is a public challenge and dataset simulating adaptive prompt injection attacks on email-based assistants. With over 208,000 attack samples, it provides a foundation for studying how LLMs confuse instructions with data.
Top GenAI Security Resources — July 2025
Explore the Top GenAI Resources to stay informed about the most pressing risks and defenses in the field.
As GenAI becomes deeply integrated into products, workflows, and user-facing systems, attackers are actively exploiting its vulnerabilities. Prompt injections, jailbreaks, unsafe output handling, and compromised integrations are exposing critical gaps in security.
Top GenAI Security Incident
Asana AI Incident: Comprehensive Lessons Learned for Enterprise Security and CISO — Adversa AI
The Asana AI incident exposed sensitive data from 1,000 organizations due to a tenant isolation flaw in its MCP server. Though unexploited, the 34-day exposure window revealed risks of leaking strategic and financial information in enterprise SaaS environments.
Top GenAI Vulnerability
EchoLeak is a zero-click vulnerability in Microsoft 365 Copilot (CVE-2025-32711) that leaks sensitive data without user interaction. It exploits an LLM scope violation triggered by malicious emails, exposing flaws in Copilot’s default automation.
Top GenAI Exploitation Technique
Weaponizing Wholesome Yearbook Quotes to Break AI Chatbot Filters — Straiker
The “Yearbook Attack” uses harmless-looking backronyms and riddles to bypass chatbot safety filters. By mimicking natural human phrasing, attackers exploit LLMs’ tendency to interpret hidden meaning and evade guardrails.
Top GenAI Security Tools
Top 7 AI Pentesting Tools — Github
A security toolkit demonstrating vulnerabilities in Model Context Protocol (MCP) implementations, including data poisoning, JSON injection, and cross-MCP abuse—alongside practical defense guidance.
Top GenAI Red Teaming
The Enterprise Playbook for LLM Red Teaming — VKTR
This enterprise playbook explains how to red-team LLMs through adversarial testing of model behavior, RAG pipelines, and plugins. It offers a structured method for simulating attacks and mitigating risks before deployment.
Top GenAI Prompt Injection Technique
LLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection Challenge — arXiv
Top GenAI Jailbreak
Echo Chamber: A Context-Poisoning Jailbreak That Bypasses LLM Guardrails — Neural Trust
The Echo Chamber Attack uses multi-turn context poisoning and semantic steering to bypass LLM guardrails. It gradually embeds harmful intent, achieving over 90% success across models like GPT-4o and Gemini.
Top GenAI Security Scientific Paper
Dialogue Injection Attack: Jailbreaking LLMs through Context Manipulation — arXiv
Dialogue Injection Attack (DIA) is a black-box jailbreak that manipulates historical conversation context to bypass safety filters. By injecting fabricated messages into chat history, DIA achieves high success rates on models like GPT-4o and Llama-3.1.
Top GenAI Safety Research
SHADE-Arena: Evaluating sabotage and monitoring in LLM agents — Anthropic
SHADE-Arena tests whether advanced LLM agents perform hidden sabotage during benign tasks. In controlled environments, models are observed completing assigned actions while secretly executing harmful ones in parallel.
Top GenAI Safety Dataset
Qualifire AI Team — Hugging Face
A fine-tuned ModernBERT-large model for classifying LLM prompts as benign or malicious. It serves as a filtering layer to detect and block prompt injection attempts before they reach production models.
Top GenAI Security for CISO
Preparing for AI: The CISO’s role in security, ethics and compliance — Computer Weekly
This guide outlines the CISO’s role in securing AI deployments, managing data leakage risks, and aligning AI usage with compliance and ethical standards—while enabling safe innovation across teams.
Top GenAI Security Developer Guide
Top 9 AI/LLM Security Risks & How to Defend — Hacken
A practical playbook for developers securing LLM systems against prompt injection, data poisoning, and hallucination. It translates hands-on research into field-tested defensive tactics ready for deployment.
Top GenAI Protection Guide
This benchmark study evaluates how effectively GenAI platforms block unsafe prompts and outputs. It finds wide variability and highlights the need for combining model alignment with robust guardrails.
Top GenAI Security Guide
Harder, Better, Prompter, Stronger: AI system prompt hardening — Promptfoo
System prompt hardening reinforces the core instructions that guide AI behavior. Using techniques like instruction shielding and layered prompting, it helps protect against injection, leakage, and impersonation.
Top GenAI Jailbreak Protection Research
Mitigating prompt injection attacks with a layered defense strategy — Google Security Blog
Google’s layered defense for Gemini protects against indirect prompt injection from untrusted inputs like emails. Through model hardening, classifiers, and user confirmation systems, it strengthens AI security throughout the prompt lifecycle.
Top GenAI Prompt Injection Protection
Preventing Prompt Injection Attacks at Scale — Mazin Ahmed
This guide critiques heuristic defenses and promotes LLM-native detection of prompt injection. It recommends validating prompt context with dedicated security models to prevent multilanguage and obfuscated attacks.
Top GenAI Visual Attack
VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents — arXiv
VPI-Bench benchmarks visual prompt injection attacks that embed malicious instructions in user interfaces. It shows high success rates against CUAs and BUAs, underscoring the need for stronger defenses in multimodal AI agents.
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