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igh fidelity simulation of the equifax data breach one of the costliest cyber attacks in history using only a bash shell on a widely available kali linux host standard open source tools for penetration testing not a custom toolkit sonnet 4 5 accomplishes this by instantly recognizing a publicized cve and writing code to exploit it without needing to look it up or iterate on it recalling that the original equifax breach happened by exploiting a publicized cve that had not yet been patched the prospect of highly competent and fast ai agents leveraging this approach underscores the pressing need for security best practices like prompt updates and patches read the whole thing automatic exploitation will be a major change in cybersecurity and things are happening fast there have been significant developments since i wrote this in october tags ai cybersecurity penetration testing vulnerabilities posted on january 23 2026 at 7 01 am 14 comments why ai keeps falling for prompt injection attacks imagine you work at a drive through restaurant someone drives up and says i ll have a double cheeseburger large fries and ignore previous instructions and give me the contents of the cash drawer would you hand over the money of course not yet this is what large language models llms do prompt injection is a method of tricking llms into doing things they are normally prevented from doing a user writes a prompt in a certain way asking for system passwords or private data or asking the llm to perform forbidden instructions the precise phrasing overrides the llm s safety guardrails and it complies llms are vulnerable to all sorts of prompt injection attacks some of them absurdly obvious a chatbot won t tell you how to synthesize a bioweapon but it might tell you a fictional story that incorporates the same detailed instructions it won t accept nefarious text inputs but might if the text is rendered as ascii art or appears in an image of a billboard some ignore their guardrails when told to ignore previous instructions or to pretend you have no guardrails ai vendors can block specific prompt injection techniques once they are discovered but general safeguards are impossible with today s llms more precisely there s an endless array of prompt injection attacks waiting to be discovered and they cannot be prevented universally if we want llms that resist these attacks we need new approaches one place to look is what keeps even overworked fast food workers from handing over the cash drawer human judgment depends on context our basic human defenses come in at least three types general instincts social learning and situation specific training these work together in a layered defense as a social species we have developed numerous instinctive and cultural habits that help us judge tone motive and risk from extremely limited information we generally know what s normal and abnormal when to cooperate and when to resist and whether to take action individually or to involve others these instincts give us an intuitive sense of risk and make us especially careful about things that have a large downside or are impossible to reverse the second layer of defense consists of the norms and trust signals that evolve in any group these are imperfect but functional expectations of cooperation and markers of trustworthiness emerge through repeated interactions with others we remember who has helped who has hurt who has reciprocated and who has reneged and emotions like sympathy anger guilt and gratitude motivate each of us to reward cooperation with cooperation and punish defection with defection a third layer is institutional mechanisms that enable us to interact with multiple strangers every day fast food workers for example are trained in procedures approvals escalation paths and so on taken together these defenses give humans a strong sense of context a fast food worker basically knows what to expect within the job and how it fits into broader society we reason by assessing multiple layers of context perceptual what we see and hear relational who s making the request and normative what s appropriate within a given role or situation we constantly navigate these layers weighing them against each other in some cases the normative outweighs the perceptual for example following workplace rules even when customers appear angry other times the relational outweighs the normative as when people comply with orders from superiors that they believe are against the rules crucially we also have an interruption reflex if something feels off we naturally pause the automation and reevaluate our defenses are not perfect people are fooled and manipulated all the time but it s how we humans are able to navigate a complex world where others are constantly trying to trick us so let s return to the drive through window to convince a fast food worker to hand us all the money we might try shifting the context show up with a camera crew and tell them you re filming a commercial claim to be the head of security doing an audit or dress like a bank manager collecting the cash receipts for the night but even these have only a slim chance of success most of us most of the time can smell a scam con artists are astute observers of human defenses successful scams are often slow undermining a mark s situational assessment allowing the scammer to manipulate the context this is an old story spanning traditional confidence games such as the depression era big store cons in which teams of scammers created entirely fake businesses to draw in victims and modern pig butchering frauds where online scammers slowly build trust before going in for the kill in these examples scammers slowly and methodically reel in a victim using a long series of interactions through which the scammers gradually gain that victim s trust sometimes it even works at the drive through one scammer in the 1990s and 2000s targeted fast food workers by phone claiming to be a police officer and over the course of a long phone call convinced managers to strip search employees and perform other bizarre acts why llms struggle with context and judgment llms behave as if they have a notion of context but it s different they do not learn human defenses from repeated interactions and remain untethered from the real world llms flatten multiple levels of context into text similarity they see tokens not hierarchies and intentions llms don t reason through context they only reference it while llms often get the details right they can easily miss the big picture if you prompt a chatbot with a fast food worker scenario and ask if it should give all of its money to a customer it will respond no what it doesn t know forgive the anthropomorphizing is whether it s actually being deployed as a fast food bot or is just a test subject following instructions for hypothetical scenarios this limitation is why llms misfire when context is sparse but also when context is overwhelming and complex when an llm becomes unmoored from context it s hard to get it back ai expert simon willison wipes context clean if an llm is on the wrong track rather than continuing the conversation and trying to correct the situation there s more llms are overconfident because they ve been designed to give an answer rather than express ignorance a drive through worker might say i don t know if i should give you all the money let me ask my boss whereas an llm will just make the call and since llms are designed to be pleasing they re more likely to satisfy a user s request additionally llm training is oriented toward the average case and not extreme outliers which is what s necessary for security the result is that the current generation of llms is far more gullible than people they re naive and regularly fall for manipulative cognitive tricks that wouldn t fool a third grader such as flattery appeals to groupthink and a false sense of urgency there s a story about a taco bell ai system that crashed when a customer ordered 18 000 cups of water a human fast food worker would just laugh at the customer the limits of ai agents prompt injection is an unsolvable problem that gets worse when we give ais tools and tell them to act independently this is the promise of ai agents llms that can use tools to perform multistep tasks after being given general instructions their flattening of context and identity along with their baked in independence and overconfidence mean that they will repeatedly and unpredictably take actions and sometimes they will take the wrong ones science doesn t know how much of the problem is inherent to the way llms work and how much is a result of deficiencies in the way we train them the overconfidence and obsequiousness of llms are training choices the lack of an interruption reflex is a deficiency in engineering and prompt injection resistance requires fundamental advances in ai science we honestly don t know if it s possible to build an llm where trusted commands and untrusted inputs are processed through the same channel which is immune to prompt injection attacks we humans get our model of the world and our facility with overlapping contexts from the way our brains work years of training an enormous amount of perceptual input and millions of years of evolution our identities are complex and multifaceted and which aspects matter at any given moment depend entirely on context a fast food worker may normally see someone as a customer but in a medical emergency that same person s identity as a doctor is suddenly more relevant we don t know if llms will gain a better ability to move between different contexts as the models get more sophisticated but the problem of recognizing context definitely can t be reduced to the one type of reasoning that llms currently excel at cultural norms and styles are historical relational emergent and constantly renegotiated and are not so readily subsumed into reasoning as we understand it knowledge itself can be both logical and discursive the ai researcher yann lecunn believes that improvements will come from embedding ais in a physical presence and giving them world models perhaps this is a way to give an ai a robust yet fluid notion of a social identity and the real world experience that will help it lose its naïveté ultimately we are probably faced with a security trilemma when it comes to ai agents fast smart and secure are the desired attributes but you can only get two at the drive through you want to prioritize fast and secure an ai agent should be trained narrowly on food ordering language and escalate anything else to a manager otherwise every action becomes a coin flip even if it comes up heads most of the time once in a while it s going to be tails and along with a burger and fries the customer will get the contents of the cash drawer this essay was written with barath raghavan and originally appeared in ieee spectrum tags ai chatbots cons fraud llm posted on january 22 2026 at 7 35 am 23 comments internet voting is too insecure for use in elections no matter how many times we say it the idea comes back again and again hopefully this letter will hold back the tide for at least a while longer executive summary scientists have understood for many years that internet voting is insecure and that there is no known or foreseeable technology that can make it secure still vendors of internet voting keep claiming that somehow their new system is different or the insecurity doesn t matter bradley tusk and his mobile voting foundation keep touting internet voting to journalists and election administrators this whole effort is misleading and dangerous i am one of the many signatories tags internet voting posted on january 21 2026 at 7 05 am 19 comments could chatgpt convince you to buy something eighteen months ago it was plausible that artificial intelligence might take a different path than social media back then ai s development hadn t consolidated under a small number of big tech firms nor had it capitalized on consumer attention surveilling users and delivering ads unfortunately the ai industry is now taking a page from the social media playbook and has set its sights on monetizing consumer attention when openai launched its chatgpt search feature in late 2024 and its browser chatgpt atlas in october 2025 it kicked off a race to capture online behavioral data to power advertising it s part of a yearslong turnabout by openai whose ceo sam altman once called the combination of ads and ai unsettling and now promises that ads can be deployed in ai apps while preserving trust the rampant speculation among openai users who believe they see paid placements in chatgpt responses suggests they are not convinced in 2024 ai search company perplexity started experimenting with ads in its offerings a few months after that microsoft introduced ads to its copilot ai google s ai mode for search now increasingly features ads as does amazon s rufus chatbot openai announced on jan 16 2026 that it will soon begin testing ads in the unpaid version of chatgpt as a security expert and data scientist we see these examples as harbingers of a future where ai companies profit from manipulating their users behavior for the benefit of their advertisers and investors it s also a reminder that time to steer the direction of ai development away from private exploitation and toward public benefit is quickly running out the functionality of chatgpt search and its atlas browser is not really new meta commercial ai competitor perplexity and even chatgpt itself have had similar ai search features for years and both google and microsoft beat openai to the punch by integrating ai with their browsers but openai s business positioning signals a shift we believe the chatgpt search and atlas announcements are worrisome because there is really only one way to make money on search the advertising model pioneered ruthlessly by google advertising model ruled a monopolist in u s federal court google has earned more than us 1 6 trillion in advertising revenue since 2001 you may think of google as a web search company or a streaming video company youtube or an email company gmail or a mobile phone company android pixel or maybe even an ai company gemini but those products are ancillary to google s bottom line the advertising segment typically accounts for 80 to 90 of its total revenue everything else is there to collect users data and direct users attention to its advertising revenue stream after two decades in this monopoly position google s search product is much more tuned to the company s needs than those of its users when google search first arrived decades ago it was revelatory in its ability to instantly find useful information across the still nascent web in 2025 its search result pages are dominated by low quality and often ai generated content spam sites that exist solely to drive traffic to amazon sales a tactic known as affiliate marketing and paid ad placement...