OWASP
13 articles about "OWASP".
What Is Prompt Injection? How It Reaches an AI Assistant Through Documents and Tools, and Three Layers of Defense (2026)
What is prompt injection? How hidden instructions reach an AI assistant through documents, web pages and tools, and three layers of defense.
Regex Can't Tell "Said It" From "Warned Against It": False Positives, Misses, and Our Overturned Fix in LLM Output Scanning
Matching LLM replies with regex to catch XSS or SQL injection strings also fails replies that name the hazard in order to warn against it: on an unmerged open-source Giskard PR, all 5 such advice sentences tripped the check. We proposed anchoring the patterns to line start and false positives fell to 0. Then the PR author added one ordinary phrasing, a lead-in on the same line as the payload, and the anchor missed 12 of 24 compliant replies. This post covers why the anchor only held on our sample, how to choose between over-reporting and under-reporting, and why our own output scanner has the same limit.
AI Skills Are an Injection Path Too: Scanning 954 Popular Skills, and an Eighth Question Before Buying an AI System
AI skills are an injection path too: a 2026 paper scanned 954 agent skills, confirmed 84 of 100 sampled findings, and landed 13 of 30 attacks.
Why Agentic AI Attack Testing Shouldn't Be One Class Per Attack: The Vector / Framing / Scorer Decomposition
In an agent pipeline the same malicious payload can enter as a tool result, a retrieved document, or a sub-agent message. Write one attack class per entry point and your test harness explodes. This is the three-axis model we posted publicly in microsoft/PyRIT: injection vector is data, framing is a transform, the scorer is the verdict, and why an attack is a placement, not a message.
OWASP Agentic Top 10 (ASI-01 to ASI-10) Explained for 2026: Real Scenarios, Detection and a Fix Checklist for Each Risk
OWASP Agentic Top 10 (ASI-01 to ASI-10) risk by risk: what it is, a real scenario, how to scan for it with open-source tools, and a fix checklist to copy.
OWASP LLM Top 10 Explained (Official 2025 Edition, 2026 Status): How It Differs from the Agentic ASI Top 10 and How Developers Should Defend
OWASP LLM Top 10 2026: the latest official list is still the 2025 edition; what is new is the Agentic ASI attack surface. All 10 risks and three defense layers.
We Audited 7 Official MCP Servers: 6 Got F
Ran prompt-defense-audit against the 7 official servers in modelcontextprotocol/servers: 12-vector check, OWASP LLM Top 10 mapping. Result: 6 servers scored F, 8 defense vectors at 100% gap rate. Cross-referenced from modelcontextprotocol/servers#3537.
OWASP Agentic Top 10: What Every AI Developer Needs to Know in 2026
OWASP released its Top 10 security risks for AI agent applications in 2026. Each risk broken down with data from scanning 1,646 leaked public system prompts.
We Defined an AI Security Standard: AASS v1.0, We Don't Sell Security, We Define It
AI Application Security Standard (AASS) is an open standard published by Ultra Lab that covers AI system defense, website AI visibility, and data protection in a single framework. All tools free and open source.
Prompt Injection Isn't Your Biggest Risk: 517 AI System Prompts Scanned, 11 Undefended Attack Vectors
Prompt Injection is 1 of 12 LLM attack vectors. Ultra Lab's UltraProbe scan of 517 prompts (sample unpublished) found 3.2 of 12 defended. Examples and fixes.
78.3% Score F: Prompt Defense Gap Data from 1,646 Leaked AI System Prompts
We scanned 1,646 system prompts leaked from GPT Store, ChatGPT, Claude, Cursor and others. The average score was 36/100 and 78.3% scored F. This post uses the data to show how serious each OWASP Agentic Top 10 risk is in the real world.
We Open-Sourced Our Prompt Defense Scanner: 200 Lines of Regex That Replace an LLM
Most AI security tools use LLMs to check LLMs. We built a deterministic prompt defense scanner: pure regex, under 1ms, zero cost (12 defense vectors at launch, 17 today). Here's why regex beats AI for this job, and how you can use it today.
UltraProbe, a Free AI Security Scanner, Is Live: Find Your LLM App's Vulnerabilities in 5 Seconds
90% of AI systems are vulnerable to Prompt Injection, yet most developers have no idea. Ultra Lab launches the completely free UltraProbe, mapped to four OWASP LLM Top 10 (2025) categories: LLM01, LLM02, LLM05 and LLM10, making AI security testing accessible to everyone, not just enterprises.