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4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems — 1 src1.3 Paper proposes evolving context parameterization for large language models — 1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems — 1 src1.3 Paper proposes evolving context parameterization for large language models — 1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src
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#adversarial-ml

4 curated events
papersTODAY 04:00 UTC

Study Reexamines How Effective Targeted Data Poisoning Attacks Really Are

A new arXiv paper argues that common evaluations of targeted data poisoning attacks are misleading because they average success rates across randomly chosen test targets, which masks worst-case outcomes. The author(s) suggest that this averaging can overstate or understate the practical threat depending on the specific samples an adversary cares about. The work calls for evaluation protocols that account for per-target variation rather than relying on aggregate scores.

data-poisoning/adversarial-ml/safety/benchmarks
papersSEP 10 04:00 UTC

Study Probes Why Adversarial Examples Transfer Between Deepfake Detectors

A new arXiv paper examines the conditions under which adversarial inputs crafted against one deepfake detector also fool other detectors an attacker has never seen. The work focuses on how the relationship between the surrogate model used to generate attacks and the target model influences attack success. The findings could inform the design of detectors that better withstand such black-box attacks.

deepfakes/adversarial-ml/security/research
papersSEP 10 04:00 UTC

Paper evaluates adversarial training for tabular credit scoring robustness in P2P lending

Researchers have published an evaluation of how adversarial training affects the robustness of tabular machine learning credit scoring models used in peer-to-peer lending. The study tests these models against multiple attack types that simulate applicants tweaking self-reported information to influence lending decisions. The work highlights a security gap in financial ML systems that depend on user-provided inputs.

adversarial-ml/fintech/robustness/credit-scoring
papersSEP 10 04:00 UTC

New arXiv Paper Proposes Bayesian Adversarial Privacy, a Context-Specific Privacy Measure

A revised arXiv research paper introduces a quantitative definition of privacy designed to be tailored to specific settings rather than serving as a uniform guarantee. The framework combines Bayesian reasoning with adversarial modeling to gauge how much sensitive information an attacker could infer. The work positions itself as a unifying contribution amid the wide range of diverging approaches in privacy research.

privacy/adversarial-ml/theory