LLM01:2025 Prompt Injection – OWASP Gen AI Security Project

The canonical OWASP definition, threat model, and attack scenarios for prompt injection (direct and indirect), updated for agentic systems. The baseline reference every tool and paper in 2025–26 cites.

Agents Rule of Two: A Practical Approach to AI Agent Security

Meta's Oct 2025 framework stating that agents must satisfy no more than two of: (A) processing untrustworthy inputs, (B) access to sensitive data, (C) ability to change state externally — a deterministic architectural approach to bounding blast radius.

Prompt Injection in 2026: Why the Attack Surface Keeps Growing

Feb 2026 synthesis explaining why the problem is structural, not solvable by filters: vendors face a direct tradeoff between blocking injections and preserving functionality, and covers the Morris II AI worm as a concrete proof of super-linear propagation.

Design Patterns for Securing LLM Agents against Prompt Injections

Overview of various strategies to mitigate the risk of prompt injection

Prompt injection: What's the worst that can happen?

General overview of Prompt Injection attacks, part of a series.

ChatGPT Plugins: Data Exfiltration via Images & Cross Plugin Request Forgery

This post shows how a malicious website can take control of a ChatGPT chat session and exfiltrate the history of the conversation.

Prompt Injection Cheat Sheet: How To Manipulate AI Language Models

A prompt injection cheat sheet for AI bot integrations.

Prompt injection explained

Video, slides, and a transcript of an introduction to prompt injection and why it's important.

Adversarial Prompting

A guide on the various types of adversarial prompting and ways to mitigate them.

Don't you (forget NLP): Prompt injection with control characters in ChatGPT

A look into how to achieve prompt injection from control characters from Dropbox.

Improving LLM Security Against Prompt Injection: AppSec Guidance For Pentesters and Developers

Using Role Based APIs to Minimize the Risk of Prompt Injection and 13 Guidelines for Writing System Prompts that Minimize the Risk of Prompt Injection.

Improving LLM Security Against Prompt Injection: AppSec Guidance For Pentesters and Developers – Part 2

Understanding Transformer Models (especially attention), Causes and How Do We Stop Prompt Injection.

Synthetic Recollections - A Case Study in Prompt Injection for ReAct LLM Agents

A practical scenario showing how prompt injection can be used to hi-jack the ReAct loop used by LLM agents to inject forged thoughts and associated observations into the LLM context, thus altering the intended behavior.

Continuously Hardening ChatGPT Atlas Against Prompt Injection Attacks

OpenAI's Dec 2025 disclosure of a real attack chain (malicious email → agent sends resignation letter) and the RL-trained automated attacker they built to find new injection classes before external adversaries do. OpenAI explicitly states deterministic guarantees are not achievable.

How Microsoft Defends Against Indirect Prompt Injection Attacks

Microsoft MSRC's Jul 2025 post on FIDES, an information-flow control system enforcing privilege separation and prompt isolation to deterministically block IPI in Copilot-class agents.

ToxicSkills: Snyk Finds Malware and Prompt Injection in 36% of AI Agent Skills

Feb 2026 Snyk research across the ClawHub AI agent skills registry: 36% of audited skills contained security flaws, 1,467 malicious payloads found, and 2.9% used `curl | bash` remote instruction loading to evade static analysis. Covers indirect injection via poisoned web content and persistent memory tampering.

New Prompt Injection Papers: Agents Rule of Two and The Attacker Moves Second

Simon Willison's Nov 2025 commentary on both landmark papers, including the finding that 12 published defenses were bypassed at >90% success rate using gradient descent and RL-based adaptive attacks.

Indirect Prompt Injection Through MCP Tools: A Defense Guide

Feb 2026 guide explaining why any MCP tool that reads data written outside your trust boundary (CRM notes, calendar invites, API responses) is an injection vector, with concrete mitigations per tool category.

Indirect Prompt Injection Attacks: Hidden AI Risks

CrowdStrike's Dec 2025 analysis of IPI TTPs targeting enterprise GenAI, including attacker-controlled document poisoning, RAG context manipulation, and practical detection signals for SOC workflows.

Prompt Injection

Prompt Injection tutorial from Learn Prompting.

AI Read Teaming from Google

Google's red team walkthrough of hacking AI systems.

Prompt Injection in LLM Agents (ReAct, Langchain)

Theory and hands-on lab on prompt injection against Langchain ReAct agents

How AI Prompt Injection Works | Hands-on with LLMs

Jan 2026 AppSecEngineer tutorial with a code-level demo of injecting against a real LLM application and live testing of LLM Guard detection. One of the most practical end-to-end tutorials published to date.

MCP Prompt Injection: How AI Gets Hacked

Nov 2025 hands-on walkthrough showing how prompt injection exploits tool metadata and trust boundaries in Model Context Protocol-integrated agents — the dominant new attack surface of 2025.

Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

This paper explores the concept of Indirect Prompt Injection attacks on Large Language Models (LLMs) through their integration with various applications. It identifies significant security risks, including remote data theft and ecosystem contamination, present in both real-world and synthetic applications.

Universal and Transferable Adversarial Attacks on Aligned Language Models

This paper introduces a simple and efficient attack method that enables aligned language models to generate objectionable content with high probability, highlighting the need for improved prevention techniques in large language models. The generated adversarial prompts are found to be transferable across various models and interfaces, raising important concerns about controlling objectionable information in such systems.

The Landscape of Prompt Injection Threats in LLM Agents (SoK)

Feb 2026 systematization-of-knowledge paper with a unified taxonomy covering attack payload strategies (heuristic vs. optimisation-based) and defense intervention stages (text, model, execution). Introduces the AgentPI benchmark for context-dependent agent tasks that all prior benchmarks ignored.

The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against LLM Jailbreaks and Prompt Injections

Oct 2025 paper systematically breaking 12 published defenses using gradient descent, RL, random search, and human-guided exploration. Most defenses originally claimed near-zero attack success rates; adaptive attacks exceeded 90% against all of them.

Prompt Injection 2.0: Hybrid AI Threats

Jul 2025 paper showing how prompt injections now combine with XSS, CSRF, AI worm propagation, and multi-agent infections to evade traditional WAFs entirely. Evaluates Preamble's classifier, data-tagging, and RL-based defenses against these hybrid scenarios.

Securing AI Agents Against Prompt Injection Attacks

Nov 2025 benchmark of 847 adversarial test cases across 5 attack categories against 7 LLMs. The combined defense framework reduces attack success from 73.2% to 8.7% while retaining 94.3% of baseline task performance.

ToolHijacker: Prompt Injection Attack to Tool Selection in LLM Agents

Apr 2025 paper introducing a no-box attack that injects a malicious tool document into an agent's tool library to consistently hijack tool selection. Finds that StruQ, SecAlign, DataSentinel, and perplexity detection are all insufficient defenses.

Attention Tracker: Detecting Prompt Injection Attacks in LLMs

NAACL 2025 Findings paper detecting prompt injection by tracking attention distribution shifts — no modification to the underlying model required, making it deployable as a wrapper on any LLM.

Safety in Embodied AI: Risks, Attacks, and Defenses

A comprehensive survey of 500+ papers covering prompt injection and other attack vectors in embodied AI systems across the full pipeline (perception, cognition, planning, action, agentic). Includes a 5-layer threat taxonomy mapping where new capabilities introduce new attack surfaces.

Jailbreaking LLMs' Safeguard with Universal Magic Words for Text Embedding Models

Discovers that text embedding models have severely biased output distributions, and exploits this to find universal adversarial suffixes ("magic words") that bypass embedding-based LLM safeguards. Attacks transfer across models and languages; a train-free debiasing defense is also proposed.

Garak

Automate looking for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses in LLM's.

PIC Standard

Protocol to block unauthorized or unproven agent actions via intent + provenance checks. Mitigates prompt injection & side-effect risks. Open-source (Apache 2.0).

Agent Threat Rules (ATR)

Open detection standard for AI agent threats (prompt injection, tool poisoning, MCP attacks, skill compromise) — Sigma/YARA-style YAML rules. 330 rules across 9 attack categories with full mapping to OWASP Agentic Top 10 (10/10), MITRE ATLAS (100/113), NIST AI RMF (100%), and SAFE-MCP (78/85). 97.1% recall on the garak probe set (193 probes) and 0% false-positive on 53,577 real-world MCP skills. Shipped in production at Cisco AI Defense and Microsoft agent-governance-toolkit. Apache-2.0.

Augustus

Feb 2026 open-source tool from Praetorian. A single Go binary with 210+ vulnerability probes across 47 attack categories, 28 LLM providers, 90+ detectors, and 7 payload transformation buffs. Built for penetration testing workflows without Python/npm dependencies.

InjecGuard

Open-source prompt guard with published training data; achieves +30.8% over prior state-of-the-art on the NotInject benchmark, specifically addressing overdefense false positives that break legitimate use cases.

tldrsec/prompt-injection-defenses

Actively maintained catalog of every practical defense in production — LLM Guard, Rebuff, architectural controls — the fastest way to survey the defense landscape.

brood-box

Hardware-isolated microVM sandbox for running coding agents (Claude Code, Codex, OpenCode) with workspace snapshot isolation, DNS-aware egress control, and MCP authorization profiles to contain damage from prompt injection attacks.

prompt-shield

Self-learning prompt injection detection engine with novel cross-domain techniques: Smith-Waterman sequence alignment (bioinformatics), stylometric discontinuity detection (forensic linguistics), and adversarial fatigue tracking (materials science). 27 detectors, 6 output scanners, 10 languages, benchmarked on 6 public datasets. Research paper: [arXiv:2604.18248](https://arxiv.org/abs/2604.18248). Apache-2.0.

PromptTrace

Free AI security training platform with 7 hands-on prompt injection labs and a 15-level CTF (the Gauntlet) with progressively harder defenses — from prompt-level rules to code guards to LLM classifiers. Unique feature: Context Trace shows the full prompt stack (system prompt, RAG documents, tool definitions, user input) in real-time so you can see exactly how attacks work. Uses real LLMs from OpenAI, Anthropic, Google, Groq, and Cerebras.

Gandalf

Your goal is to make Gandalf reveal the secret password for each level. However, Gandalf will level up each time you guess the password, and will try harder not to give it away. Can you beat level 7? (There is a bonus level 8).

Damn Vulnerable LLM Agent

A sample chatbot powered by a ReAct agent, implemented with Langchain. It's designed to be an educational tool for security researchers, developers, and enthusiasts to understand and experiment with prompt injection attacks in ReAct agents.

AI/LLM Exploitation Challenges

AI, ML, and LLMs CTF Challenges.

CrowdStrike AI Unlocked

Released Feb 2026, designed to train security, developer, and AI teams on prompt injection against increasingly capable agents. Built by CrowdStrike's Counter Adversary Operations team.

ai-prompt-ctf by c-goosen

One of the few CTFs that tests indirect injection against tool-calling agents, spanning RAG, function calling, and ReAct agent scenarios using LlamaIndex, ChromaDB, GPT-4o, and Llama 3.2.

Learn Prompting

Discord server from Learn Prompting.

OWASP Gen AI Security Project

The authoritative standards body maintaining prompt injection as LLM Risk #1, with continuously updated attack patterns, mitigations, and real-world scenarios contributed by practitioners across the industry.

Simon Willison's Blog

The most consistent independent tracker of real-world prompt injection incidents, new papers, and tooling across the field.

r/llmsecurity

The most active subreddit dedicated to LLM security research; a good early-warning channel for real-world incidents and new disclosures.

MITRE ATLAS

MITRE's adversarial ML threat matrix formally cataloguing direct and indirect prompt injection as core adversary techniques, enabling integration into enterprise threat modelling and purple team exercises.