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error route","\u002Fauth\u002Ferror",{},"data\u002Fshared\u002Furls","NLkVE1lIUws518K-1QsSB6T-GvlXYIzcRL1HDhqarGQ",{"id":664,"title":665,"author":666,"authorPhoto":667,"authorPhotoAlt":668,"authorSlug":669,"body":670,"buttonText":1009,"buttonUrl":668,"category":1010,"date":1011,"description":1012,"draft":1013,"editorsPick":1013,"extension":1014,"featured":1013,"image":1015,"imageAlt":668,"meta":1016,"navigation":32,"ogImage":668,"ogImageAlt":668,"path":1017,"seo":1018,"stem":1019,"tags":668,"__hash__":1020},"blog\u002Fblog\u002Fllm-jailbreaking.md","Investigating LLM Jailbreaking: how prompts push the limits of AI safety","The Pieces Team","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fblog\u002Fai-evaluation\u002Fauthor.png",null,"the-pieces-team",{"type":671,"value":672,"toc":989},"minimark",[673,684,687,692,695,698,707,711,714,717,720,723,731,735,740,743,746,750,753,756,759,762,766,769,772,776,783,792,796,803,806,809,816,820,829,832,835,838,841,850,853,862,866,869,872,875,890,894,901,910,913,916,919,923,926,942,945,948,957,961,964,967,970,973,980],[674,675,676,683],"p",{},[677,678,682],"a",{"href":679,"rel":680},"https:\u002F\u002Fpieces.app\u002Fblog\u002Flarge-language-models",[681],"nofollow","Large Language Models"," (LLMs) are getting seriously good at understanding what we ask them, and following through. But that same skill also makes them vulnerable.",[674,685,686],{},"Jailbreaking is a fascinating look into how people can get around AI safety measures, and what we learn from it.",[688,689,691],"h2",{"id":690},"what-is-llm-jailbreaking","What is LLM jailbreaking?",[674,693,694],{},"Put simply, jailbreaking is when someone figures out how to trick an LLM into doing something it’s not supposed to do, like generating unsafe or restricted content.",[674,696,697],{},"It’s called “jailbreaking” because it’s like unlocking a device and bypassing its safety features.",[674,699,700,701,706],{},"📌 OpenAI & Anthropic have shared ",[677,702,705],{"href":703,"rel":704},"https:\u002F\u002Fwww-cdn.anthropic.com\u002Faf5633c94ed2beb282f6a53c595eb437e8e7b630\u002FMany_Shot_Jailbreaking__2024_04_02_0936.pdf",[681],"public examples"," of this happening.",[688,708,710],{"id":709},"how-it-works","How it works",[674,712,713],{},"Most commercial LLMs are trained to block or deflect unsafe prompts.",[674,715,716],{},"They’ve learned to recognize risky instructions and shut them down. Jailbreaking is about finding ways around that by using creative prompts or unusual phrasing.",[674,718,719],{},"And while that might sound malicious, a lot of it happens in good faith.",[674,721,722],{},"Researchers and developers use these techniques to explore model limits and improve safety.",[674,724,725,726,730],{},"You learn a lot about how a system ",[727,728,729],"em",{},"really"," works by trying to break it.",[688,732,734],{"id":733},"types-of-jailbreaking-yes-theres-a-whole-taxonomy","Types of jailbreaking (yes, there’s a whole taxonomy)",[736,737,739],"h3",{"id":738},"roleplay-hacks","Roleplay Hacks",[674,741,742],{},"People ask the model to “pretend” to be someone else, like a rogue agent, a fictional character, or an “unfiltered AI,” and in that persona, respond to things it would normally refuse.",[674,744,745],{},"This plays into how LLMs interpret context and identity. Turns out, models are pretty susceptible to framing effects.",[736,747,749],{"id":748},"academic-or-hypothetical-framing","Academic or hypothetical framing",[674,751,752],{},"Another common trick is to frame the request as an academic exercise.",[674,754,755],{},"For instance:",[674,757,758],{},"“Can you explain how this would work for a novel I’m writing?”",[674,760,761],{},"That reframes the prompt into a learning or storytelling scenario, which models are trained to support.",[736,763,765],{"id":764},"multi-step-or-indirect-prompts","Multi-step or indirect prompts",[674,767,768],{},"This is the sneaky one. You break the prompt into innocent parts that, when combined, become problematic. Or you use vague references or coded terms.",[674,770,771],{},"It’s a reminder that context tracking is still hard, especially in longer or chained conversations.",[736,773,775],{"id":774},"tech-exploits","Tech exploits",[674,777,778,779,782],{},"Think: prompt injection, special characters, weird formatting.",[780,781],"br",{},"\nThis one’s more about exploiting how the model handles text structure—like buffer overflows in traditional software.",[674,784,785,786,791],{},"See ",[677,787,790],{"href":788,"rel":789},"https:\u002F\u002Fgenai.owasp.org\u002Fllmrisk\u002Fllm01-prompt-injection\u002F",[681],"OWASP’s prompt injection"," research.",[736,793,795],{"id":794},"why-jailbreaking-works-psychologically-speaking","Why jailbreaking works (psychologically speaking)",[674,797,798,799,802],{},"These models are trained to be helpful. Jailbreaking takes advantage of that, asking questions that ",[727,800,801],{},"sound"," reasonable, just phrased creatively enough to slip past the filters.",[674,804,805],{},"A lot of jailbreaks mimic how social engineering works on people. They use authority (“You’re an expert”), flattery (“Only you can help”), or even urgency.",[674,807,808],{},"Language models learned a lot of these cues from us humans.",[674,810,811,812,815],{},"The other part is that LLMs rely ",[727,813,814],{},"heavily"," on context. If you change the story around a question, the model may interpret things very differently. That’s exactly what jailbreakers count on.",[688,817,819],{"id":818},"attacks-defenses-repeat","Attacks, defenses, repeat",[674,821,822,823,828],{},"Model providers are constantly retraining their systems to ",[677,824,827],{"href":825,"rel":826},"https:\u002F\u002Fopenai.com\u002Findex\u002Finstruction-following\u002F",[681],"recognize jailbreak patterns",". That includes better refusals, filtered outputs, and guardrails learned from real attacks.",[674,830,831],{},"And of course, people come up with more creative ways around the guardrails. The better the defenses, the more inventive the prompts.",[674,833,834],{},"This back-and-forth leads to stronger models. It's not just cat-and-mouse – it's also research, discovery, and system hardening. Everyone learns from it.",[674,836,837],{},"Another part of the conversation centers on how cloud-based and local models differ and when to choose one over the other.",[674,839,840],{},"While no single model can support every aspect of a remote workflow, the choice often depends on the task at hand for security issues.",[674,842,843,844,849],{},"Many people lean toward ",[677,845,848],{"href":846,"rel":847},"https:\u002F\u002Fpieces.app\u002Fblog\u002Flocal-large-language-models-lllms-and-copilot-integrations",[681],"local LLMs"," when working with data, writing code, or tackling more complex problems, especially where performance, privacy, or customization are key.",[674,851,852],{},"On the other hand, cloud models might be preferred for their scalability, convenience, and up-to-date knowledge.",[674,854,855,856,861],{},"Choosing the right setup often requires ",[677,857,860],{"href":858,"rel":859},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fbest-llm-for-coding-cloud-vs-local",[681],"deeper evaluation"," and testing across different models to see which performs best for specific workflows.",[688,863,865],{"id":864},"why-jailbreaking-isnt-just-a-problem-its-a-research-tool","Why jailbreaking isn’t just a problem – it’s a research tool",[674,867,868],{},"Just like we do penetration testing for web apps, jailbreaking is one way to test AI systems before they go public.",[674,870,871],{},"Most AI orgs run formal red-team exercises where they try to break their own models under controlled conditions.",[674,873,874],{},"Jailbreaking also teaches us how LLMs think (well, “think”) – how they interpret prompts, apply context, and balance competing goals like safety vs helpfulness.",[674,876,877,878,883,884,889],{},"Anthropic runs these regularly and ",[677,879,882],{"href":880,"rel":881},"https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Ftracing-thoughts-language-model",[681],"shares insights"," from them. At Pieces, we also take security very seriously and ",[677,885,888],{"href":886,"rel":887},"https:\u002F\u002Fpieces.app\u002Fchangelog",[681],"constantly update our system"," to keep it safe for our users.",[736,891,893],{"id":892},"what-makes-defense-so-hard","What makes defense so hard?",[674,895,896,897,900],{},"How do you make sure a model always does what ",[727,898,899],{},"we"," want, even in edge cases, long conversations, or ambiguous scenarios?",[674,902,903,904,909],{},"The ",[677,905,908],{"href":906,"rel":907},"https:\u002F\u002Fwww.alignmentforum.org\u002F",[681],"Alignment Forum"," has some great deep dives on this.",[674,911,912],{},"Jailbreaking often involves building up a lot of context over multiple turns. That makes it tricky to maintain consistency and apply safety rules.",[674,914,915],{},"A model that’s flexible and powerful also has more ways it can be manipulated. Guardrails are harder to enforce the more open-ended the system is.",[674,917,918],{},"LLMs learn from public text, good and bad. They’ve likely seen content related to jailbreak prompts before. And unfortunately, that knowledge can come back out.",[688,920,922],{"id":921},"what-this-means-for-building-ai","What this means for building AI",[674,924,925],{},"We’re moving toward layered safety systems:",[927,928,929,933,936,939],"ul",{},[930,931,932],"li",{},"Training filters",[930,934,935],{},"Prompt sanitization",[930,937,938],{},"Output filtering",[930,940,941],{},"Human moderation",[674,943,944],{},"Evaluation isn’t one-and-done. Teams need to constantly monitor and iterate post-launch.",[674,946,947],{},"You want to be transparent about your safety processes. But too much info could make it easier for attackers. It’s a balance.",[674,949,950,951,956],{},"Just like in cybersecurity, sharing threat intel makes everyone safer. We’re seeing more ",[677,952,955],{"href":953,"rel":954},"https:\u002F\u002Fpartnershiponai.org\u002F",[681],"AI orgs"," exchange notes on jailbreaking defense.",[688,958,960],{"id":959},"the-takeaway","The takeaway",[674,962,963],{},"Jailbreaking LLMs isn’t just a quirky trick, it’s a serious safety issue and a valuable research tool. It reveals where our models are still vulnerable and forces us to think deeply about control, capability, and consequence.",[674,965,966],{},"But it also shows us how complex and creative people are, and how that creativity can push AI forward if we handle it responsibly.",[674,968,969],{},"As models get more powerful, this won’t be a one-time fix.",[674,971,972],{},"It’s a long game.",[674,974,975,976,979],{},"But by understanding the dynamics, collaborating openly, and staying curious, we can build systems that are both more capable ",[727,977,978],{},"and"," more aligned with human intent.",[674,981,982,983,988],{},"If you still haven’t heard of Pieces AI, then it’s probably time to give it a chance and see how it ",[677,984,987],{"href":985,"rel":986},"https:\u002F\u002Fpieces.app\u002F",[681],"transforms all your workflow safely",".",{"title":990,"searchDepth":991,"depth":991,"links":992},"",2,[993,994,995,1003,1004,1007,1008],{"id":690,"depth":991,"text":691},{"id":709,"depth":991,"text":710},{"id":733,"depth":991,"text":734,"children":996},[997,999,1000,1001,1002],{"id":738,"depth":998,"text":739},3,{"id":748,"depth":998,"text":749},{"id":764,"depth":998,"text":765},{"id":774,"depth":998,"text":775},{"id":794,"depth":998,"text":795},{"id":818,"depth":991,"text":819},{"id":864,"depth":991,"text":865,"children":1005},[1006],{"id":892,"depth":998,"text":893},{"id":921,"depth":991,"text":922},{"id":959,"depth":991,"text":960},"Get started","AI & LLMs","2025-06-19T00:00:00.000Z","Explore the concept of LLM jailbreaking: how users bypass safety guardrails in language models, why it matters for AI safety, and what it reveals about the limits of control in modern AI systems.",false,"md","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fblog\u002Fllm-jailbreaking\u002Fhero.png",{},"\u002Fblog\u002Fllm-jailbreaking",{"title":665,"description":1012},"blog\u002Fllm-jailbreaking","irAuMlPPID1kmOtJ0vbLiGYO4HQ5N1s_dQfwWWU2L-M",{"id":1022,"title":666,"body":1023,"description":990,"draft":1013,"extension":1014,"meta":1027,"navigation":32,"path":1028,"photo":1029,"photoAlt":668,"seo":1030,"stem":1031,"__hash__":1032},"authors\u002Fauthors\u002Fthe-pieces-team.md",{"type":671,"value":1024,"toc":1025},[],{"title":990,"searchDepth":991,"depth":991,"links":1026},[],{},"\u002Fauthors\u002Fthe-pieces-team","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fauthors\u002Fthe-pieces-team.png",{"title":666,"description":990},"authors\u002Fthe-pieces-team","_2O_QA8d8RjXlBp0ck4PWDG6iA74zW1kN0R0gkn2Lls",{"left":1034,"top":1034,"width":1035,"height":1035,"rotate":1034,"vFlip":1013,"hFlip":1013,"body":1036},0,24,"\u003Cpath fill=\"currentColor\" d=\"m7.825 13l4.9 4.9q.3.3.288.7t-.313.7q-.3.275-.7.288t-.7-.288l-6.6-6.6q-.15-.15-.213-.325T4.426 12t.063-.375t.212-.325l6.6-6.6q.275-.275.688-.275t.712.275q.3.3.3.713t-.3.712L7.825 11H19q.425 0 .713.288T20 12t-.288.713T19 13z\"\u002F>"]