Red teaming LLMs exposes a harsh truth about the AI security arms race
Unrelenting, persistent attacks on frontier models make them fail, with the patterns of failure varying by model and developer. Red teaming shows that it’s not the sophisticated, complex attacks that can bring a model down; it’s the attacker automating continuous, random attempts that will inevitably force a model to fail.That’s the harsh truth that AI apps and platform builders need to plan for as they build each new release of their products. Betting an entire build-out on a frontier model prone to red team failures due to persistency alone is like building a house on sand. Even with red teaming, frontier LLMs, including those with open weights, are lagging behind adversarial and weaponized AI.The arms race has already startedCybercrime costs reached $9.5 trillion in 2024 and forec [...]
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Anthropic vs. OpenAI red teaming methods reveal different security priorities for enterprise AI
Model providers want to prove the security and robustness of their models, releasing system cards and conducting red-team exercises with each new release. But it can be difficult for enterprises to pa [...]
Multi-turn attacks broke AI models 88% of the time — single-turn testing missed it, Cisco AI security lead warns at VB Transform 2026
When Cisco ran 6,986 multi-turn attacks against 15 flagship models, attackers who adapted across the conversation broke through as often as 88.3% of the time. Amy Chang, Cisco's head of AI threat [...]
OpenAI admits prompt injection is here to stay as enterprises lag on defenses
It's refreshing when a leading AI company states the obvious. In a detailed post on hardening ChatGPT Atlas against prompt injection, OpenAI acknowledged what security practitioners have known fo [...]
Prompt Security's Itamar Golan on why generative AI security requires building a category, not a feature
VentureBeat recently sat down (virtually) with Itamar Golan, co-founder and CEO of Prompt Security, to chat through the GenAI security challenges organizations of all sizes face. We talked about shado [...]
Every leading AI agent failed at least one security test during a massive red teaming competition
A major red teaming study has uncovered critical security flaws in today's AI agents. Every system tested from leading AI labs failed to uphold its own security guidelines under attack.<br /&g [...]
Three AI coding agents leaked secrets through a single prompt injection. One vendor's system card predicted it
A security researcher, working with colleagues at Johns Hopkins University, opened a GitHub pull request, typed a malicious instruction into the PR title, and watched Anthropic’s Claude Code Securit [...]
Four AI supply-chain attacks in 50 days exposed the release pipeline red teams aren't covering
Four supply-chain incidents hit OpenAI, Anthropic and Meta in 50 days: three adversary-driven attacks and one self-inflicted packaging failure. None targeted the model, and all four exposed the same g [...]
Mythos autonomously exploited vulnerabilities that survived 27 years of human review. Security teams need a new detection playbook
A 27-year-old bug sat inside OpenBSD’s TCP stack while auditors reviewed the code, fuzzers ran against it, and the operating system earned its reputation as one of the most security-hardened platfor [...]