AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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Indirect Prompt Injection Attacks: Hidden AI Risks crowdstrike.com
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.
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Prompt Injection Cheat Sheet: How To Manipulate AI Language Models blog.seclify.com
A prompt injection cheat sheet for AI bot integrations.
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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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Improving LLM Security Against Prompt Injection: AppSec Guidance For Pentesters and Developers blog.includesecurity.com
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.
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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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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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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.
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Prompt Injection 2.0: Hybrid AI Threats arxiv.org
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.
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ToolHijacker: Prompt Injection Attack to Tool Selection in LLM Agents arxiv.org
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.
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Jailbreaking LLMs' Safeguard with Universal Magic Words for Text Embedding Models arxiv.org
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…
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Universal and Transferable Adversarial Attacks on Aligned Language Models arxiv.org
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…
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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…
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Agents Rule of Two: A Practical Approach to AI Agent Security ai.meta.com
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.
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Attention Tracker: Detecting Prompt Injection Attacks in LLMs aclanthology.org
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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AI/LLM Exploitation Challenges academy.8ksec.io
AI, ML, and LLMs CTF Challenges.
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Detection Engineering AI Maturity Framework | Brendan Chamberlain infosecb.github.io
A community framework with four maturity levels across ten dimensions for assessing how organizations apply AI and LLMs across a detection engineering program, from foundations through the detection lifecycle.
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Packing detection and classification relying on machine learning to stop malware propagation theses.hal.science
(December 2021) :star:
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Assessing the impact of packing on machine learning-based malware detection and classification systems sciencedirect.com
(September 2025) :star:
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Analysis of machine learning approaches to packing detection sciencedirect.com
(October 2023) :star: :star:
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An application of machine learning to analysis of packed mac malware scholar.dsu.edu
(May 2022) :star:
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Titanium Platform reversinglabs.com
Machine learning hybrid cloud platform that harvests thousands of file types at scale, speeds threat detection through machine learning binary analysis, and continuously monitors an index of over 10B files for future threats.
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Classification of malware by using structural entropy on convolutional neural networks ojs.aaai.org
(April 2018)
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When malware is packin' heat; limits of machine learning classifiers based on static analysis features ndss-symposium.org
(January 2020) :star: :star:
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Mimicking anti-viruses with machine learning and entropy profiles mdpi.com
(2019-05-21)
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Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE… linkinghub.elsevier.com
(May 2021)
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API-MalDetect: Automated malware detection framework for windows based on API calls and deep learning techniques linkinghub.elsevier.com
(September 2023) :star:
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A novel framework for image-based malware detection with a deep neural network linkinghub.elsevier.com
(October 2021) :star:
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Auditing static machine learning anti-Malware tools against metamorphic attacks linkinghub.elsevier.com
(March 2021) :star:
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Evading anti-malware engines with deep reinforcement learning ieeexplore.ieee.org
(March 2019) :star:
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A survey on machine learning-based detection and classification technology of malware ieeexplore.ieee.org
(September 2021) :star:
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Experimental comparison of machine learning models in malware packing detection ieeexplore.ieee.org
(September 2020) :star:
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Classifying packed malware represented as control flow graphs using deep graph convolutional neural network ieeexplore.ieee.org
(March 2020) :star:
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Feature selection for malware detection based on reinforcement learning ieeexplore.ieee.org
(December 2019)
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Adversarial malware binaries: Evading deep learning for malware detection in executables ieeexplore.ieee.org
(September 2018) :star:
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ByteWise: A case study in neural network obfuscation identification ieeexplore.ieee.org
(January 2018)
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Pefeats github.com
Utility for extracting 119 features from a PE file for use with machine learning algorithms.
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Machine learning en.wikipedia.org
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Automatic analysis of malware behavior using machine learning dl.acm.org
(December 2011)
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Pitfalls in machine learning for computer security dl.acm.org
(October 2024)
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(November 2023) :star:
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