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Adversa AI Trusted AI Blog · April 23, 2026

Interleaved in-context learning as a structural jailbreak technique

analysis Adversarial MLAI Red TeamingModel Evaluation
Why it matters

Adversa's IICL study evaluates a few-shot jailbreak that interleaves benign and harmful demonstrations and uses short output-field labels to shift model behavior. Across more than 3,500 probes, ten models, and seven ablations, results vary materially with example order and field names; the work is vendor-authored and its model-specific attack rates should be independently reproduced.

My takeaway: Add structural few-shot attacks to safety regression suites, vary demonstration order, labels, templates, and model versions, and report both per-query and per-attempt success with uncertainty. Keep a held-out attack set and rerun it after every system-prompt, scaffold, or model change.
Keep exploring

More curated notes connected through Adversarial ML and AI Red Teaming.

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Pacing model development in an era of cyber-critical capabilities

OpenAI says preliminary evidence that Astra may meet its Critical cybersecurity threshold led it to pause frontier reinforcement-learning work for two weeks and keep its largest planned run on hold. New safeguards include stronger workload and network isolation, continuous boundary testing, token-level monitoring that escalates suspicious tool activity, and broader alignment checks for deception, reward hacking, and unauthorized access.

OWASP GenAI Security Project · guide

OWASP Top 10 for Agentic Applications for 2026

OWASP's community guide organizes agentic-system risk into ten categories, including goal hijacking, tool misuse, identity and privilege abuse, memory poisoning, insecure inter-agent communication, cascading failures, and rogue-agent behavior. It provides a shared taxonomy and mitigation starting point rather than a certification checklist or evidence that a deployed system is secure.

OpenAI News · framework

A blueprint for democratic governance of frontier AI

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