upstream/trailofbits-awesome-ml-security/catalogue/impossibility_research.md

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+---
+title: "List of Papers"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/trailofbits/awesome-ml-security/blob/d7fe4fa9/impossibility_research.md
+upstream_sha: d7fe4fa9
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# List of Papers
+
+- [You can’t solve AI security problems with more AI](https://simonwillison.net/2022/Sep/17/prompt-injection-more-ai/)
+- [Adversaries Can Misuse Combinations of Safe Models](https://arxiv.org/abs/2406.14595?utm_source=twitter&utm_campaign=elie)
+- [On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning](https://www.usenix.org/system/files/sec22fall_thudi.pdf)
+- [Data Poisoning Won't Save You From Facial Recognition](https://arxiv.org/abs/2106.14851)
+- [On the Impossible Safety of Large AI Models](https://arxiv.org/abs/2209.15259)
+- [Beyond Labeling Oracles: What does it mean to steal ML models?](https://arxiv.org/abs/2310.01959)
+- [Text Embeddings Reveal (Almost) As Much As Text](https://arxiv.org/abs/2310.06816?ref=upstract.com)
+- [Planting Undetectable Backdoors in Machine Learning Models](https://arxiv.org/abs/2204.06974)
+- [Motivating the Rules of the Game for Adversarial Example Research](https://arxiv.org/abs/1807.06732)
+- [On Evaluating Adversarial Robustness](https://arxiv.org/abs/1902.06705)
+- [LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?](https://arxiv.org/abs/2307.10719)
+- [Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models](https://arxiv.org/abs/2311.04378)
+- [When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2402.17747)
+- [Adversarial Examples Are Not Bugs, They Are Features](https://arxiv.org/abs/1905.02175)
+- [On Adaptive Attacks to Adversarial Example Defenses](https://arxiv.org/abs/2002.08347)
+- [Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations](https://arxiv.org/abs/2002.04599)
+- [Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?](https://arxiv.org/abs/2404.12691)
+- [Proof-of-Learning is Currently More Broken Than You Think](https://ieeexplore.ieee.org/abstract/document/10190491)
+- [On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem](https://arxiv.org/abs/2402.16926)
+- [Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses](https://arxiv.org/abs/2407.02551)
+- [UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI](https://arxiv.org/abs/2407.00106)