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data poisoning

topic2 events
papersTODAY 04:00 UTC

arXiv paper proposes learned selection of poison sets for LLM backdoor attacks

A new arXiv preprint introduces a method that learns which examples to poison in order to make backdoor attacks on fine-tuned language models more effective. The authors note that prior work usually holds the number of poisoned examples fixed, and their approach instead optimizes the choice of poison set. The paper appears in both cs.AI and cs.LG listings.

backdoor-attacks/llm-security/fine-tuning/research
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