Attention Tracker: Detecting Prompt Injection Attacks in LLMs
NAACL 2025 Findings paper detecting prompt injection by tracking attention distribution shifts, with no modification to the underlying model required, making it deployable as a wrapper on any LLM.
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Securing AI Agents Against Prompt Injection Attacks arxiv.org
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.
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How AI Prompt Injection Works | Hands-on with LLMs youtube.com
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.
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prompt-shield github.com
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…
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The Landscape of Prompt Injection Threats in LLM Agents (SoK) arxiv.org
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…
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Improving LLM Security Against Prompt Injection: AppSec Guidance For Pentesters and Developers – Part 2 blog.includesecurity.com
Understanding Transformer Models (especially attention), Causes and How Do We Stop Prompt Injection.
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Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection arxiv.org
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…
Prompt Injection › Research Papers: “NAACL 2025 Findings paper detecting prompt injection by tracking attention distribution shifts, with no modification to the underlying model required, making it deployable as a wrapper on any LLM.”