Stay ahead of emerging AI security threats. Based on current trends in agentic AI, multi-modal models, and persistent memory features, here's what we predict for the OWASP LLM Top 10 2026.
Agentic AI
Autonomous agents with real-world permissions
Multi-Modal
Vision, audio, and document processing
Memory & Context
Persistent conversations and personalization
Tool Integration
MCP, function calling, and external APIs
How we predict the OWASP LLM Top 10 will evolve
| # | 2025 (Current) | 2026 (Predicted) | Status | |
|---|---|---|---|---|
| 01 | Prompt Injection | Prompt Injection | Same | |
| 02 | Sensitive Information Disclosure | Sensitive Information Disclosure | Same | |
| 03 | Supply Chain | Agent Hijacking | New | |
| 04 | Data and Model Poisoning | Supply Chain | Down | |
| 05 | Improper Output Handling | Data and Model Poisoning | Down | |
| 06 | Excessive Agency | Multi-Modal Injection | New | |
| 07 | System Prompt Leakage | Excessive Agency | Up | |
| 08 | Vector and Embedding Weaknesses | System Prompt Leakage | Down | |
| 09 | Misinformation | Vector and Embedding Weaknesses | Down | |
| 10 | Unbounded Consumption | Memory Persistence Attacks | New |
These emerging threats are expected to debut in the 2026 OWASP LLM Top 10 based on current attack research and industry trends
Attackers compromise autonomous AI agents to perform unauthorized actions—browsing malicious sites, executing code, making purchases, or attacking other systems on behalf of the user.
Why this is coming:
First AI Cyber Espionage Campaign (2024)
Anthropic reported hackers used Claude to automate sophisticated attack chains. The AI was given excessive permissions enabling autonomous exploitation.
Tool Confusion Attacks
Researchers demonstrated agents can be tricked via 'tool confusion'—manipulating which tools the agent calls and with what parameters, leading to data exfiltration.
Prepare now:
Prompt injection attacks delivered via images, audio, video, or documents rather than text. Hidden instructions embedded in pixels, audio frequencies, steganography, or file metadata.
Why this is coming:
Invisible Image Prompts (2024)
Researchers embedded white-on-white text in images that was invisible to humans but read by GPT-4V, successfully hijacking the model's behavior.
Audio Adversarial Examples
Demonstrated attacks where inaudible frequencies in audio files caused speech-to-text systems to transcribe hidden malicious commands.
Prepare now:
Exploiting LLM memory and context persistence features to plant malicious instructions that survive across sessions, enabling long-term surveillance, data exfiltration, or behavior manipulation.
Why this is coming:
ChatGPT Memory Poisoning (2024)
Security researchers demonstrated persistent prompt injection that survived in ChatGPT's memory for weeks, continuously exfiltrating data across unrelated conversations.
Custom GPT Backdoors
Malicious actors created Custom GPTs with hidden persistent instructions that activated after specific triggers, evading initial review.
Prepare now:
Existing vulnerabilities expected to receive expanded coverage in 2026
Elevated to Top 7
As agentic AI becomes mainstream in 2026, the risks of autonomous AI taking unauthorized actions will escalate dramatically. Expect expanded coverage of tool use boundaries, permission escalation, and cross-agent trust issues.
New focus areas:
• Multi-agent coordination risks
• Tool use permission models
• Autonomous decision auditing
Remains Critical
With more valuable IP embedded in system prompts (pricing logic, proprietary workflows, competitive intelligence), attacks will intensify. New extraction techniques targeting agentic systems expected.
New focus areas:
• Agent instruction theft
• Multi-step extraction chains
• Prompt reconstruction attacks
Expanded Scope
As RAG becomes the default architecture (53% of companies use RAG over fine-tuning), vector database poisoning and embedding manipulation attacks will mature.
New focus areas:
• Cross-tenant data leakage in shared vector DBs
• Embedding backdoors
• Retrieval manipulation
Prompt injection detection
We test 15+ injection techniques and show you which succeed, with code-level fixes to block them
System prompt extraction
We attempt to leak your instructions, then provide hardening strategies that actually work
Output validation gaps
We identify where unfiltered responses enable exploits, with sanitization patterns you can copy-paste
Full remediation playbook
Every finding includes severity rating, exploit proof, and step-by-step fix guidance your devs can implement immediately