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· Past edition · 8 stories
Retrospective edition reconstructed from later captures. Paper versions may include later updates; coverage labels describe the evidence used.
Safety, alignment & governance
GPT‑Red uses automated self-play red-teaming to find prompt-injection failures, then feeds attacks into production-model training to improve resistance.
Why it matters: Automated red-teaming can generate attacks at a scale intended to improve robustness through training. The article presents it as a complement—not a replacement—to human and third-party red-teaming, layered safeguards, and real-time monitoring.
Safety, alignment & governance
Open each story in the reader. Summaries are shown when available; some sources provide an abstract only. With an account you can keep your own notes.
Persistent agent memory creates a prompt-injection risk: malicious instructions planted in memory files may affect not just one interaction, but future sessions too.
Why it matters: Memory changes the prompt-injection threat model: defenses need to protect memory updates while preserving useful agent adaptation, and the abstract reports that previously planted payloads can still affect later sessions.
Safety, alignment & governance
CAVA proposes a stable action object so approvals and execution can be compared across the incompatible records produced by different agent runtimes.
Why it matters: A stable action identity could give deployers a consistent basis for binding approval to execution across runtimes. The abstract presents this as a governance substrate, not evidence that the reference implementation achieves particular rates or performance.