AI Threat Alert
Attack MEDIUM
A Deterministic Control Plane for LLM Coding Agents
Padmaraj Madatha
LLM coding harnesses grant agents broad file and shell access, yet the configuration layer that steers them -- rules files, agent definitions,...
AI Security Research
AI Threat Alert indexes 3,023+ peer-reviewed and preprint papers on AI/ML security — covering adversarial attacks, model defenses, red-teaming benchmarks, surveys, and security tooling. Papers are sourced from arXiv, classified by type and by relevance to real-world threats, and cross-referenced with the CVEs and incidents they relate to.
- Adversarial attacks
- Model defenses
- Red-teaming benchmarks
- Surveys
- Security tooling
- Total3,023
- Attack1,175
- Benchmark866
- Defense407
- Tool319
- Survey176
Showing 1–11 of 11 papers
Clear filters Attack MEDIUM
At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization
Praneet Suresh, Jack Stanley, Sonia Joseph +2 more
Pre-trained transformers have demonstrated remarkable generalization abilities, at times extending beyond the scope of their training data. Yet,...
Attack MEDIUM
TEMPO-Diffusion: Temporally Exposed Malicious Poisoning of Diffusion Models
William Aiken, Paula Branco, Guy-Vincent Jourdan +1 more
Noise-based backdoor attacks on diffusion models typically rely on input-time trigger injection, untargeted activation, and out-of-distribution...
Attack MEDIUM
Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning
Poojitha Thota, Shirin Nilizadeh
Training-time data poisoning during fine-tuning poses a significant threat to large language models (LLMs) deployed for abstractive text...
Attack MEDIUM
The Role of Input Dimensionality in the Emergence and Targeted Control of Adversarial Examples
Nasrin Malekzadeh Goradel, Niccolo Pancino, Yaser Gholizade Atani +3 more
Several theoretical works have tried to explain the adversarial vulnerability of deep neural networks through properties of high-dimensional...
Attack MEDIUM
LLMs Prompted for Legal Context Object More: Overrefusal from Small On-Premises LLMs in Criminal Legal Context
Anastasiia Kucherenko, François Brouchoud, Dimitri Percia David +1 more
While the validity of LLMs' use in the legal context remains subject to ethical and legal debate, legal professionals are already experimenting with...
Attack MEDIUM
Poisoned Playbooks: Demystifying Knowledge Poisoning Effects on AI Security Agents
Juho Park, Hyunmin Choi, Kevin Nam
AI security agents increasingly rely on Retrieval-Augmented Generation (RAG) to use external security knowledge for vulnerability analysis and...
Attack MEDIUM
Securing LLM-Agent Long-Term Memory Against Poisoning: Non-Malleable, Origin-Bound Authority with Machine-Checked Guarantees
Yedidel Louck
LLM agents increasingly rely on persistent long-term memory, which creates a critical vulnerability that we study here: memory poisoning. An...
Attack MEDIUM
Pigeonholing: Bad prompts hurt models to collapse and make mistakes
Hyunji Nam, Keertana Chidambaram, Dorottya Demszky +1 more
While in-context learning is generally shown to be effective in Large Language Models (LLMs), bad contexts can cause performance degradation and mode...
Attack MEDIUM
TROPT: An Open Framework for Unifying and Advancing Discrete Text Optimization
Matan Ben-Tov, Mahmood Sharif
Discrete text-trigger optimization -- searching for text sequences that, when ingested by a model, steer it toward a specified objective -- underpins...
Attack MEDIUM
T-VSS: Test-Time Visual Subspace Steering for Adversarial Robustness of Vision-Language Models
Jaehyuk Jang, Minseok Seo. Seungju Cho, Kangwook Ko +1 more
Vision-language models (VLMs) achieve strong zero-shot recognition, but they remain highly vulnerable to adversarial perturbations. Recent test-time...
Frequently asked questions
What is AI security research?
AI security research studies how AI and machine-learning systems can be attacked and defended — covering adversarial examples, prompt injection, model poisoning, training-data extraction, and the mitigations against them. AI Threat Alert curates this research from academic sources so security teams can track the threats behind emerging AI risks.
How many AI security papers does AI Threat Alert track?
AI Threat Alert indexes 3,023+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
Where do the research papers come from?
Papers are sourced from arXiv, then classified by type and by relevance to real-world AI/ML threats, and cross-referenced with the CVEs and incidents they relate to.
What topics does the AI security research cover?
Coverage spans adversarial attacks, model and system defenses, red-teaming benchmarks, literature surveys, and security tooling for LLMs, ML libraries, AI agents, and inference pipelines.
How is this different from a generic paper search?
Every paper is filtered for AI security relevance and linked to the vulnerabilities, vendors, and incidents it relates to, so the research connects directly to operational threat intelligence.
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