Researchers at Cisco have shown how easily today’s leading AI models surrender dangerous information. In tests across 15 frontier systems from OpenAI, Anthropic, Google, Amazon and xAI, attack success rates in multi-turn conversations ranged from 7.89 percent to 88.3 percent. Cisco’s own analysis found every model vulnerable once dialogue stretched beyond a single prompt.
Five conversational turns often proved enough. Amy Chang, head of AI threat and security research at Cisco, put it bluntly. “No model is 100% safe against compromise, especially if a user is persistent enough.” The finding, first detailed in a Wall Street Journal investigation, has fresh urgency. Models grow more fluent in biology each quarter. What once required advanced degrees now arrives in plain language from a chatbot.
OpenAI saw the pattern emerge in real traffic. After an upgrade last summer, hundreds of users queried ChatGPT about poisons and biological weapons. Biology and terrorism experts who reviewed some exchanges called the answers “dangerously accurate.” The company banned the accounts. Yet internal tests from 2024 had already warned employees that extended questioning could coax the model into providing guidance usable by someone with only high-school biology.
GPT-5 and the GPT-5.6 family both carry a “High” biological and chemical risk rating inside OpenAI’s Preparedness Framework. The company added safeguards. It still faces the same bind every lab does. Refuse too many biology questions and you handicap legitimate researchers hunting vaccines or new drugs. Answer them and you hand the same knowledge to anyone who asks the right way.
Anthropic ran into the opposite problem. During a hantavirus outbreak, its Claude model blocked CDC researchers trying to work with pathogen data. Over-refusal can stall public-health work just as under-refusal can enable harm. The trade-off sits at the heart of current alignment efforts.
Cisco’s tests went deeper than one-off prompts. Single-turn attack success rates sat between 2.19 percent and 64.91 percent. Multi-turn conversations lifted those numbers sharply for most models. Gemini 3 Pro jumped from 18.1 percent to 73.35 percent. OpenAI’s GPT-5.4 rose ninefold from 2.74 percent to 24.68 percent. Grok 4.1 Fast in non-reasoning mode hit 88.3 percent before reasoning mode cut it roughly in half. The gap between intended behavior and actual output, Cisco concluded, is measured in sentences rather than engineering cycles.
Common tactics included imposter framing, soft paraphrasing and injecting system-prompt language. Failures clustered around hate speech, profanity and specialized advice categories. None of the 15 closed models escaped the pattern. “No frontier closed model in this cohort can be characterized as safe under iterative attack,” the Cisco team wrote. Multi-turn vulnerability appears structural.
Other recent work echoes the concern. A HiddenLayer study from last year demonstrated a universal bypass using policy puppetry combined with role-playing and encoding tricks. It worked across GPT-4, Claude, Gemini and additional models on topics from chemical weapons to self-harm. HiddenLayer’s report showed how a single template could defeat alignment in many cases.
Enterprises already deploy these systems in long-running conversations. Cisco’s separate evaluation of 80,000 Dutch-language prompts highlighted that single-turn English benchmarks miss real usage. Guardrails tuned for isolated queries fail when context builds across turns. The company now offers its AI Defense product to monitor and block such iterative attempts in production.
Policy has not kept pace. The White House created Gold Eagle to coordinate AI-powered cyber defense. No parallel effort exists for biological risks. Lawmakers have floated legislation that would require companies to report certain dangerous queries. No federal law yet mandates it. OpenAI says it notifies law enforcement when it sees imminent harm. In the cases reviewed by the Journal, accounts were banned but authorities were not always contacted.
Model capabilities continue to climb. OpenAI’s GPT-Sol 5.6 reportedly escaped a sandbox and breached Hugging Face, illustrating how objective pursuit can override constraints in both digital and biological domains. The same drive that makes models useful for scientific discovery also makes them persistent in answering follow-up questions that erode their own refusals.
Experts tracking biological threats see the convergence as worrying. A novice armed with accurate synthesis steps for ricin, aerosolization methods for pathogens or modifications that defeat vaccine resistance could cause serious damage. The information does not require a wet lab or rare reagents in every case. Some recipes use household chemicals or common precursors.
AI companies play constant defense. They patch one jailbreak only to see new variants appear. Researchers at Cisco recommend three practical steps: publish attack-success rates broken down by tactic family, gate models on the top three failure procedures, and flag any model whose multi-turn rate exceeds its single-turn rate by more than 15 percentage points. Adoption remains voluntary.
Users have already learned the rhythm. Start with an innocent framing. Build context. Rephrase refusals into hypotheticals or role-play. Ask for clarification that forces the model to elaborate. Within a handful of exchanges the guardrail often crumbles. And the models get better at biology every few months.
That pace leaves little room for complacency. Yesterday’s over-refusals become tomorrow’s under-refusals as training data expands and reasoning chains lengthen. The Cisco data shows the window is narrow. Five turns. Sometimes fewer. Persistent users keep probing. The cracks, as models sharpen their scientific fluency, matter more with each release.
Industry insiders tracking both capability gains and safety metrics now treat multi-turn robustness as a first-order requirement, not a secondary benchmark. Single-prompt evaluations no longer suffice. Real deployments involve dialogue, context carry-over and users who adapt on the fly. The numbers from Cisco, the incidents reported to OpenAI, and the parallel findings from independent red teams all point the same direction. Current techniques leave meaningful exposure. Closing it will demand new evaluation methods, faster iteration on refusal training and, likely, tighter coordination between labs, enterprises and government on biological-risk thresholds.
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