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route","\u002Fthank-you",{},"data\u002Fshared\u002Furls","P27xKEauu8D-8sfyr0wR4giF0teFSaCuAQ8kgcICQdI",{"id":590,"title":591,"author":592,"authorPhoto":593,"authorPhotoAlt":594,"authorSlug":595,"body":596,"buttonText":934,"buttonUrl":594,"category":935,"date":936,"description":937,"draft":938,"editorsPick":938,"extension":939,"featured":938,"image":940,"imageAlt":594,"meta":941,"navigation":20,"ogImage":594,"ogImageAlt":594,"path":942,"seo":943,"stem":944,"tags":594,"__hash__":945},"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":597,"value":598,"toc":914},"minimark",[599,610,613,618,621,624,633,637,640,643,646,649,657,661,666,669,672,676,679,682,685,688,692,695,698,702,709,718,722,729,732,735,742,746,755,758,761,764,767,776,779,788,792,795,798,801,815,819,826,835,838,841,844,848,851,867,870,873,882,886,889,892,895,898,905],[600,601,602,609],"p",{},[603,604,608],"a",{"href":605,"rel":606},"https:\u002F\u002Fpieces.app\u002Fblog\u002Flarge-language-models",[607],"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.",[600,611,612],{},"Jailbreaking is a fascinating look into how people can get around AI safety measures, and what we learn from it.",[614,615,617],"h2",{"id":616},"what-is-llm-jailbreaking","What is LLM jailbreaking?",[600,619,620],{},"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.",[600,622,623],{},"It’s called “jailbreaking” because it’s like unlocking a device and bypassing its safety features.",[600,625,626,627,632],{},"📌 OpenAI & Anthropic have shared ",[603,628,631],{"href":629,"rel":630},"https:\u002F\u002Fwww-cdn.anthropic.com\u002Faf5633c94ed2beb282f6a53c595eb437e8e7b630\u002FMany_Shot_Jailbreaking__2024_04_02_0936.pdf",[607],"public examples"," of this happening.",[614,634,636],{"id":635},"how-it-works","How it works",[600,638,639],{},"Most commercial LLMs are trained to block or deflect unsafe prompts.",[600,641,642],{},"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.",[600,644,645],{},"And while that might sound malicious, a lot of it happens in good faith.",[600,647,648],{},"Researchers and developers use these techniques to explore model limits and improve safety.",[600,650,651,652,656],{},"You learn a lot about how a system ",[653,654,655],"em",{},"really"," works by trying to break it.",[614,658,660],{"id":659},"types-of-jailbreaking-yes-theres-a-whole-taxonomy","Types of jailbreaking (yes, there’s a whole taxonomy)",[662,663,665],"h3",{"id":664},"roleplay-hacks","Roleplay Hacks",[600,667,668],{},"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.",[600,670,671],{},"This plays into how LLMs interpret context and identity. Turns out, models are pretty susceptible to framing effects.",[662,673,675],{"id":674},"academic-or-hypothetical-framing","Academic or hypothetical framing",[600,677,678],{},"Another common trick is to frame the request as an academic exercise.",[600,680,681],{},"For instance:",[600,683,684],{},"“Can you explain how this would work for a novel I’m writing?”",[600,686,687],{},"That reframes the prompt into a learning or storytelling scenario, which models are trained to support.",[662,689,691],{"id":690},"multi-step-or-indirect-prompts","Multi-step or indirect prompts",[600,693,694],{},"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.",[600,696,697],{},"It’s a reminder that context tracking is still hard, especially in longer or chained conversations.",[662,699,701],{"id":700},"tech-exploits","Tech exploits",[600,703,704,705,708],{},"Think: prompt injection, special characters, weird formatting.",[706,707],"br",{},"\nThis one’s more about exploiting how the model handles text structure—like buffer overflows in traditional software.",[600,710,711,712,717],{},"See ",[603,713,716],{"href":714,"rel":715},"https:\u002F\u002Fgenai.owasp.org\u002Fllmrisk\u002Fllm01-prompt-injection\u002F",[607],"OWASP’s prompt injection"," research.",[662,719,721],{"id":720},"why-jailbreaking-works-psychologically-speaking","Why jailbreaking works (psychologically speaking)",[600,723,724,725,728],{},"These models are trained to be helpful. Jailbreaking takes advantage of that, asking questions that ",[653,726,727],{},"sound"," reasonable, just phrased creatively enough to slip past the filters.",[600,730,731],{},"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.",[600,733,734],{},"Language models learned a lot of these cues from us humans.",[600,736,737,738,741],{},"The other part is that LLMs rely ",[653,739,740],{},"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.",[614,743,745],{"id":744},"attacks-defenses-repeat","Attacks, defenses, repeat",[600,747,748,749,754],{},"Model providers are constantly retraining their systems to ",[603,750,753],{"href":751,"rel":752},"https:\u002F\u002Fopenai.com\u002Findex\u002Finstruction-following\u002F",[607],"recognize jailbreak patterns",". That includes better refusals, filtered outputs, and guardrails learned from real attacks.",[600,756,757],{},"And of course, people come up with more creative ways around the guardrails. The better the defenses, the more inventive the prompts.",[600,759,760],{},"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.",[600,762,763],{},"Another part of the conversation centers on how cloud-based and local models differ and when to choose one over the other.",[600,765,766],{},"While no single model can support every aspect of a remote workflow, the choice often depends on the task at hand for security issues.",[600,768,769,770,775],{},"Many people lean toward ",[603,771,774],{"href":772,"rel":773},"https:\u002F\u002Fpieces.app\u002Fblog\u002Flocal-large-language-models-lllms-and-copilot-integrations",[607],"local LLMs"," when working with data, writing code, or tackling more complex problems, especially where performance, privacy, or customization are key.",[600,777,778],{},"On the other hand, cloud models might be preferred for their scalability, convenience, and up-to-date knowledge.",[600,780,781,782,787],{},"Choosing the right setup often requires ",[603,783,786],{"href":784,"rel":785},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fbest-llm-for-coding-cloud-vs-local",[607],"deeper evaluation"," and testing across different models to see which performs best for specific workflows.",[614,789,791],{"id":790},"why-jailbreaking-isnt-just-a-problem-its-a-research-tool","Why jailbreaking isn’t just a problem – it’s a research tool",[600,793,794],{},"Just like we do penetration testing for web apps, jailbreaking is one way to test AI systems before they go public.",[600,796,797],{},"Most AI orgs run formal red-team exercises where they try to break their own models under controlled conditions.",[600,799,800],{},"Jailbreaking also teaches us how LLMs think (well, “think”) – how they interpret prompts, apply context, and balance competing goals like safety vs helpfulness.",[600,802,803,804,809,810,814],{},"Anthropic runs these regularly and ",[603,805,808],{"href":806,"rel":807},"https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Ftracing-thoughts-language-model",[607],"shares insights"," from them. At Pieces, we also take security very seriously and ",[603,811,813],{"href":395,"rel":812},[607],"constantly update our system"," to keep it safe for our users.",[662,816,818],{"id":817},"what-makes-defense-so-hard","What makes defense so hard?",[600,820,821,822,825],{},"How do you make sure a model always does what ",[653,823,824],{},"we"," want, even in edge cases, long conversations, or ambiguous scenarios?",[600,827,828,829,834],{},"The ",[603,830,833],{"href":831,"rel":832},"https:\u002F\u002Fwww.alignmentforum.org\u002F",[607],"Alignment Forum"," has some great deep dives on this.",[600,836,837],{},"Jailbreaking often involves building up a lot of context over multiple turns. That makes it tricky to maintain consistency and apply safety rules.",[600,839,840],{},"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.",[600,842,843],{},"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.",[614,845,847],{"id":846},"what-this-means-for-building-ai","What this means for building AI",[600,849,850],{},"We’re moving toward layered safety systems:",[852,853,854,858,861,864],"ul",{},[855,856,857],"li",{},"Training filters",[855,859,860],{},"Prompt sanitization",[855,862,863],{},"Output filtering",[855,865,866],{},"Human moderation",[600,868,869],{},"Evaluation isn’t one-and-done. Teams need to constantly monitor and iterate post-launch.",[600,871,872],{},"You want to be transparent about your safety processes. But too much info could make it easier for attackers. It’s a balance.",[600,874,875,876,881],{},"Just like in cybersecurity, sharing threat intel makes everyone safer. We’re seeing more ",[603,877,880],{"href":878,"rel":879},"https:\u002F\u002Fpartnershiponai.org\u002F",[607],"AI orgs"," exchange notes on jailbreaking defense.",[614,883,885],{"id":884},"the-takeaway","The takeaway",[600,887,888],{},"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.",[600,890,891],{},"But it also shows us how complex and creative people are, and how that creativity can push AI forward if we handle it responsibly.",[600,893,894],{},"As models get more powerful, this won’t be a one-time fix.",[600,896,897],{},"It’s a long game.",[600,899,900,901,904],{},"But by understanding the dynamics, collaborating openly, and staying curious, we can build systems that are both more capable ",[653,902,903],{},"and"," more aligned with human intent.",[600,906,907,908,913],{},"If you still haven’t heard of Pieces AI, then it’s probably time to give it a chance and see how it ",[603,909,912],{"href":910,"rel":911},"https:\u002F\u002Fpieces.app\u002F",[607],"transforms all your workflow safely",".",{"title":915,"searchDepth":916,"depth":916,"links":917},"",2,[918,919,920,928,929,932,933],{"id":616,"depth":916,"text":617},{"id":635,"depth":916,"text":636},{"id":659,"depth":916,"text":660,"children":921},[922,924,925,926,927],{"id":664,"depth":923,"text":665},3,{"id":674,"depth":923,"text":675},{"id":690,"depth":923,"text":691},{"id":700,"depth":923,"text":701},{"id":720,"depth":923,"text":721},{"id":744,"depth":916,"text":745},{"id":790,"depth":916,"text":791,"children":930},[931],{"id":817,"depth":923,"text":818},{"id":846,"depth":916,"text":847},{"id":884,"depth":916,"text":885},"Get started","AI & LLM","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":591,"description":937},"blog\u002Fllm-jailbreaking","Juknr8vsKj1F-8TcQM9eJtjU1EZQtXCqJu0X6ZjGLmM",{"id":947,"title":592,"body":948,"description":915,"draft":938,"extension":939,"meta":952,"navigation":20,"path":953,"photo":954,"photoAlt":594,"seo":955,"stem":956,"__hash__":957},"authors\u002Fauthors\u002Fthe-pieces-team.md",{"type":597,"value":949,"toc":950},[],{"title":915,"searchDepth":916,"depth":916,"links":951},[],{},"\u002Fauthors\u002Fthe-pieces-team","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fauthors\u002Fthe-pieces-team.png",{"title":592,"description":915},"authors\u002Fthe-pieces-team","_2O_QA8d8RjXlBp0ck4PWDG6iA74zW1kN0R0gkn2Lls"]