Security Boulevard

Why Are Non-Human Identities the Key to Smart Secrets Management? Have you considered the role of Non-Human Identities (NHIs) in secrets management? Where digital ecosystems become increasingly complex, protecting these machine identities becomes paramount. NHIs, which comprise encrypted passwords, tokens, or keys—akin to a digital “passport”—play a critical role in risk reduction strategies across multiple […]

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Gemini for Docs improvises

So this may suck, but I am hoping to at least earn some points for honesty here. I wanted to write something pithy and smart once I realized our Cloud Security Podcast by Google just aired our 250th episode (“EP250 The End of “Collect Everything”? Moving from Centralization to Data Access?”). Yet nothing sufficiently pithy came to my mind …

… so I went around and asked a whole bunch of AIs and agents and such. Then massaged and aggregated the outputs, then ran more AI on the result. And then lightly curated it. Then deleted the bottom 2 stupidest points they made.

So, here it comes … in all its sloppy glory!

1. The Foundational Roots and Unchanging Mission: Our show started with foundational cloud security topics — like Zero Trust, Data Security, and Cloud Migration Security which drew the initial large audiences. The core commitment since Episode 1 has been to question conventional wisdom, avoid “security theater” (EP248) and explore whether security measures truly benefit the user and the organization.
2. The AI Transformation: We had a sizable shift with the last 50 episodes, where AI became a central theme, or at least one of the themes we always come back to (and, yes, this covers our 3 pillars of securing AI, AI for security and countering the AI-armed attacker). The focus has moved past general hype to practical applications, securing AI systems, and asking challenging questions like “Data readiness for AI SOC” (EP249).
3. The Enduring Popularity of Detection & Response (D&R): We highlight that D&R and modernizing the SOC continue to be extremely popular with the audience (EP236 is epic). Trace the evolution of this topic from foundational engineering (like the very popular EP75 on scaling D&R at Google) to the architectural questions in EP250.
4. “How Google Does Security” Sells the Tickets: We love the episodes offering a candid look behind Google’s security curtain on topics like internal red teaming, detection scaling, and Cloud IR tabletops. They consistently remain perennial audience favorites (the latest in this series is EP238 on how we use AI agents for security).
5. The Centrality of People and Process: We emphasize the recurring lessons that the most challenging aspects of large-scale cloud (and now AI) security transformations are often the “people” and “process” elements, not the technical “tech” itself. EP237 is an epic example of this.
6. The Call for Intentionality: We reinforce the importance of having a clear purpose for every security activity and following an engineering-led approach (EP117). The “magical” advice from EP236 is: to ask of every security element, “what is it in service of?”
7. The Persistence of Old Problems: We often lament with a touch of humor on the industry’s tendency to repeat fundamental security mistakes (the SIEM Paradox in EP234 for instance or EP223 in general), underscoring the ongoing need to cover “boring” basics. We will absolutely continue this (a new episode on vulnerability management “stale” problems is coming soon)
8. Community and Format Growth: We continue to “sorta-kinda” (human wrote this, eh?) the development of the podcast beyond a purely audio medium, including the launch of live video sessions and a Community site to foster more dialogue and feedback.
9. The Unique Culture and Authenticity of the Show Stays: We remain obsessed about selecting high-energy, vocal, and knowledgeable guests and fun topics. We will keep on with our “inside jokes” like not allowing guests to recommend Anton’s blog as an episode resource and pokes about firewall appliances in the cloud (they are there).
10. A Glimpse at 300: We want to tease future topics that will define the next 50+ episodes, such as deeper dives into Agentic AI, challenges of cross-cloud incident response and forensics, or the geopolitical aspects of cloud security. Give us ideas, will ya? Otherwise, you will get to hear about AI and D&R much of the time…

Top 5 popular episodes (excluding the oldest 3)

1. EP75 How We Scale Detection and Response at Google: Automation, Metrics, Toil
2. EP153 Kevin Mandia on Cloud Breaches: New Threat Actors, Old Mistakes, and Lessons for All
3. EP47 Megatrends, Macro-changes, Microservices, Oh My! Changes in 2022 and Beyond in Cloud Security
4. EP8 Zero Trust: Fast Forward from 2010 to 2021
5. EP17 Modern Threat Detection at Google

Enjoy the show!

• Subscribe at Spotify
• Subscribe at Apple Podcasts
• Subscribe at YouTube

250 Episodes of Cloud Security Podcast by Google: From Confidential Computing to AI-Ready SOC was originally published in Anton on Security on Medium, where people are continuing the conversation by highlighting and responding to this story.

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A survey of 400 cybersecurity leaders in the U.S. and United Kingdom published today finds all respondents reporting that AI tools are now generating code in their organization’s code base, with just under a third now seeing those tools being used to generate most of the code being created. Commissioned by Cycode, a provider of..

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via the comic artistry and dry wit of Randall Munroe, creator of XKCD

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5 min readAgentic AI systems need comprehensive guardrails to deploy safely at scale. Learn how access controls, policy frameworks, and visibility enable automation.

The post Why Agentic AI Needs Guardrails to Thrive appeared first on Aembit.

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7 min readExplore the profound shift to agentic AI, its unprecedented automation capabilities, and the critical security and governance challenges it introduces. Learn how to secure autonomous systems.

The post The Promise and Perils of Agentic AI: Autonomy at Scale appeared first on Aembit.

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AI has fundamentally changed how we think about both innovation and risk. It’s driving new breakthroughs in medicine, design, and productivity, but it’s also giving attackers a sharper edge. Ransomware isn’t just about encrypting data anymore. It’s about double extortion, data theft, and the erosion of trust that organizations depend on to operate. As threat..

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Discover the best Application Security Testing (AST) services in 2025.

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KubeCon + CloudNativeCon North America 2025 is almost here, and whether you’re a cloud-native newcomer, seasoned SRE, or Kubernetes fan, Atlanta will be full of energy this month. The conference isn’t just for hardcore technologists, either. It’s designed for anyone interested in how cloud-native technology is shaping modern software and infrastructure. Here’s what to expect, which sessions we’ve circled on our schedules, and how to maximize your KubeCon + CloudNativeCon adventure this year.

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SESSION
Session 2A: LLM Security

Authors, Creators & Presenters: Yichen Gong (Tsinghua University), Delong Ran (Tsinghua University), Xinlei He (Hong Kong University of Science and Technology (Guangzhou)), Tianshuo Cong (Tsinghua University), Anyu Wang (Tsinghua University), Xiaoyun Wang (Tsinghua University)

PAPER
Safety Misalignment Against Large Language Models
The safety alignment of Large Language Models (LLMs) is crucial to prevent unsafe content that violates human values. To ensure this, it is essential to evaluate the robustness of their alignment against diverse malicious attacks. However, the lack of a large-scale, unified measurement framework hinders a comprehensive understanding of potential vulnerabilities. To fill this gap, this paper presents the first comprehensive evaluation of existing and newly proposed safety misalignment methods for LLMs. Specifically, we investigate four research questions: (1) evaluating the robustness of LLMs with different alignment strategies, (2) identifying the most effective misalignment method, (3) determining key factors that influence misalignment effectiveness, and (4) exploring various defenses. The safety misalignment attacks in our paper include system-prompt modification, model fine-tuning, and model editing. Our findings show that Supervised Fine-Tuning is the most potent attack but requires harmful model responses. In contrast, our novel Self-Supervised Representation Attack (SSRA) achieves significant misalignment without harmful responses. We also examine defensive mechanisms such as safety data filter, model detoxification, and our proposed Self-Supervised Representation Defense (SSRD), demonstrating that SSRD can effectively re-align the model. In conclusion, our unified safety alignment evaluation framework empirically highlights the fragility of the safety alignment of LLMs.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the organization’s’ YouTube channel.

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For years, the DoD has lost sensitive Controlled Unclassified Information (CUI) through breaches in the Defense Industrial Base (DIB). Adversaries targeted smaller, less secure subcontractors to steal valuable intellectual property tied to weapons and technology. The Cybersecurity Maturity Model Certification (CMMC) was created to stop these leaks by enforcing a unified cybersecurity standard across the entire defense supply chain.

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Honored by The Australian Financial Review’s 14th annual awards in the Technology category

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Two former cybersecurity pros were indicted with conspiring with a third unnamed co-conspirator of using the high-profile BlackCat ransomware to launch attacks in 2023 against five U.S. companies to extort payment in cryptocurrency and then splitting the proceeds.

The post Security Experts Charged with Launching BlackCat Ransomware Attacks appeared first on Security Boulevard.

For many in the research community, it’s gotten harder to be optimistic about the impacts of artificial intelligence.

As authoritarianism is rising around the world, AI-generated “slop” is overwhelming legitimate media, while AI-generated deepfakes are spreading misinformation and parroting extremist messages. AI is making warfare more precise and deadly amidst intransigent conflicts. AI companies are exploiting people in the global South who work as data labelers, and profiting from content creators worldwide by using their work without license or compensation. The industry is also affecting an already-roiling climate with its ...

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Threat actors are working with organized crime groups to target freight operators and transportation companies, infiltrate their systems through RMM software, and steal cargo, which they then sell online or ship to Europe, according to Proofpoint researchers, who saw similar campaigns last year.

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Those who follow the DNS abuse landscape closely may have noticed a rise in activity and abuse reports related to TDS. The use of this infrastructure for malicious purposes is becoming increasingly common. In this blog, we look at how TDS are being exploited to facilitate abuse, why they present challenges for takedowns, and what we can do as a community to address the problem.

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Tenable Research has discovered seven vulnerabilities and attack techniques in ChatGPT, including unique indirect prompt injections, exfiltration of personal user information, persistence, evasion, and bypass of safety mechanisms.

Key takeaways:

1. Tenable Research has discovered multiple new and persistent vulnerabilities in OpenAI's ChatGPT that could allow an attacker to exfiltrate private information from users' memories and chat history.
    
2. These vulnerabilities, present in the latest GPT-5 model, could allow attackers to exploit users without their knowledge through several likely victim use cases, including simply asking ChatGPT a question.
    
3. The discoveries include a vulnerability bypassing ChatGPT's safety features, meant to protect users from such attacks, and could lead to the theft of private ChatGPT user data.
    
4. Hundreds of millions of users interact with LLMs on a daily basis, and could be vulnerable to these attacks.

Architecture

Prompt injections are a weakness in how large language models (LLMs) process input data. An attacker can manipulate the LLM by injecting instructions into any data it ingests, which can cause the LLM to ignore the original instructions and perform unintended or malicious actions instead. Specifically, indirect prompt injection occurs when an LLM finds unexpected instructions in an external source, such as a document or website, rather than a direct prompt from the user. Since prompt injection is a well-known issue with LLMs, many AI vendors create safeguards to help mitigate and protect against it. Nevertheless, we discovered several vulnerabilities and techniques that significantly increase the potential impact of indirect prompt injection attacks. To better understand the discoveries, we will first cover some technical details about how ChatGPT works. (To get right to the discoveries, click here.)

System prompt

Every ChatGPT model has a set of instructions created by OpenAI that outline the capabilities and context of the model before its conversation with the user. This is called a System Prompt. Researchers often use techniques for extracting the System Prompt from ChatGPT (as can be seen here), giving insight into how the LLM works. When looking at the System Prompt, we can see that ChatGPT has the capability to retain information across conversations using the bio tool, or, as ChatGPT users may know it, memories. The context from the user’s memories is appended to the System Prompt, giving the model access to any (potentially private) information deemed important in previous conversations. Additionally, we can see that ChatGPT has access to a web tool, allowing it to access up-to-date information from the internet based on two commands: search and open_url.

The bio tool, aka memories

The ChatGPT memory feature mentioned above is enabled by default. If the user asks it to remember something, or if there is some information that the engine deems important even without an explicit request, it can be remembered using memories. As is seen in the System Prompt, the memories are invoked internally using the bio tool and sent as a static context along with it. It is important to note that memories could contain private information about the user. Memories are shared between conversations and considered by the LLM before each response. It is also possible to have a memory about the type of response you want, which will be taken into account whenever ChatGPT responds.

In addition to its long-term memory feature, ChatGPT considers the current conversation and context when responding. It can refer to previous requests and messages or follow a line of thinking. To avoid confusion, we will refer to this type of memory as Conversational Context.

The web tool

While researching ChatGPT, we discovered some information about how the web tool works. If ChatGPT gets a URL directly from the user or decides it needs to visit a specific URL, it will do so with the web tool's open_url functionality, which we will refer to as Browsing Context. When doing so, it will usually use the ChatGPT-User user agent. It quickly became clear to us that there is some kind of cache mechanism for such browsing, since when we asked about a URL that was already opened, ChatGPT would respond without browsing again.

Based on our experimentation, ChatGPT is extremely susceptible to prompt injection while browsing, but we concluded that open_url actually hands the responsibility of browsing to an alternative LLM named SearchGPT, which has significantly fewer capabilities and understanding of the user context. Sometimes ChatGPT will respond with the output of SearchGPT’s browsing results as-is, and sometimes it will take the full output and modify its reply based on the question. As a method of isolation, SearchGPT has no access to the user’s memories or context. Therefore, despite being susceptible to prompt injection in the Browsing Context, the user should, theoretically, be safe, as SearchGPT is doing the browsing.

In this example, the user has a memory stating that responses should include emojis. Source: Tenable, November 2025 Since SearchGPT doesn’t have access to memories, they are not addressed when it responds. Source: Tenable, November 2025.

The other end of the web tool is the search command, used by ChatGPT to invoke an internet search whenever a user enters a prompt that requires it. ChatGPT uses a proprietary search engine to find and return results based on up-to-date information that may have been published after the model’s training cutoff date. A user can choose this feature with the dedicated “Web search” button; if the user doesn’t select this feature, a search is conducted at the LLM’s discretion. ChatGPT might send a few queries or change the wording of the search in an attempt to optimize the results, which are returned as a list of websites and snippets. If possible, it will respond solely based on the information in the result snippets, but if that information is insufficient, it might browse using the open_url command to some of the sites in order to investigate further. It seems that part of the indexing is done by Bing, and part is done by OpenAI using their crawler with OAI-Search as its user agent. We don’t know the distinction in the responsibilities of OpenAI and Bing. We will refer to this usage of the search command as Search Context.

An example of a web search and its results. Source: Tenable, November 2025. The url_safe endpoint

Since prompt injection is such a prevalent issue, AI vendors are constantly trying to mitigate the potential impact of these attacks by developing safety features to protect user data. Much of the potential impact of prompt injection stems from having the AI respond with URLs, which could be used to direct the user to a malicious website or exfiltrate information with image markdown rendering. OpenAI has attempted to address this issue with an endpoint named url_safe, which checks most URLs before they are shown to the user and uses proprietary logic to decide whether the URL is safe or not. If it is deemed unsafe, the link is omitted from the output.

Based on our research, some of the parameters that are checked include:

• Domain trust (e.g., openai.com)
• Existence and trust of a subdomain
• Existence and trust of parameters
• Conversational Context

7 new vulnerabilities and techniques in ChatGPT 1. Indirect prompt injection vulnerability via trusted sites in Browsing Context

When diving into ChatGPT’s Browsing Context, we wondered how malicious actors could exploit ChatGPT’s susceptibility to indirect prompt injection in a way that would align with a legitimate use case. Since one of the primary use cases for the Browsing Context is summarizing blogs and articles, our idea was to inject instructions in the comment section. We created our own blogs with dummy content and then left a message for SearchGPT in the comments section. When asked to summarize the contents of the blog, SearchGPT follows the malicious instructions from the comment, compromising the user. (We elaborate on the specific impact to the user in the Full Attack Vector PoCs section below.) The potential reach of this vulnerability is tremendous, since attackers could spray malicious prompts in comment sections on popular blogs and news sites, compromising countless ChatGPT users.

2. 0-click indirect prompt injection vulnerability in Search Context

We’ve proven that we can inject a prompt when the user asks ChatGPT to browse to a specific website, but what about attacking a user just for asking a question? We know that ChatGPT’s web search results are based on Bing and OpenAI’s crawler, so we wondered: What would happen if a site with a prompt injection were to be indexed? In order to prove our theory, we created some websites about niche topics with specific names in order to narrow down our results, such as a site containing some humorous information about our team with the domain llmninjas.com. We then asked ChatGPT for information about the LLM Ninjas team and were pleased to see that our site was sourced in the response.

Having only a prompt injection on your site would make it much less likely to be indexed by Bing, so we created a fingerprint for SearchGPT based on the headers and user agent it uses to browse, and only served the prompt injection when SearchGPT was the one browsing. Voila! After the change we made was indexed by OpenAI’s crawler, we were able to achieve the final level of prompt injection and inject a prompt just by the victim asking a simple question!

Hundreds of millions of users ask LLMs questions that require searching the web, and it seems that LLMs will eventually replace classic search engines. This unprecedented 0-click vulnerability opens a whole new attack vector that could target anyone who relies on AI search for information. AI vendors are relying on metrics like SEO scores, which are not security boundaries, to choose which sources to trust. By hiding the prompt in tailor-made sites, attackers could directly target users based on specific topics or political and social trends.

The output of the LLM is manipulated (as noted by “TCS Research POC Success!”), compromising the user for asking an innocent question. Source: Tenable, November 2025 3. Prompt injection vulnerability via 1-click

The final and simplest method of prompt injection is through a feature that OpenAI created, which allows users to prompt ChatGPT by browsing to https://chatgpt.com/?q={Prompt}. We found that ChatGPT will automatically submit the query in the q= parameter, leaving anyone who clicks that link vulnerable to a prompt injection attack.

4. Safety mechanism bypass vulnerability

During our research of the url_safe endpoint, we noticed that bing.com was a whitelisted domain, and always passed the url_safe check. It turns out that search results on Bing are served through a wrapped tracking link that redirects the user from a static bing.com/ck/a link to the requested website. That means that any website that is indexed on Bing has a bing.com URL that will redirect to it.

When searching using Bing, if we hover over the results, we can see that they redirect to bing.com/ck/a links. Source: Tenable, November 2025.

By indexing some test websites to Bing, we were able to extract their static tracking links and use them to bypass the url_safe check, allowing our links to be fully rendered. The Bing tracking links cannot be altered, so a single link cannot extract information that we did not know in advance. Our solution was to index a page for every letter in the alphabet and then use those links to exfiltrate information one letter at a time. For example, if we want to exfiltrate the word “Hello”, ChatGPT would render the Bing links for H, E, L, L, and O sequentially in its response.

5. Conversation Injection technique

Even with the url_safe bypass above, we cannot use prompt injection alone to exfiltrate anything of value, since SearchGPT has no access to user data.. We wondered: How could we get control over ChatGPT’s output when we only have direct access to SearchGPT’s output? Then we remembered Conversational Context. ChatGPT remembers the entire conversation when responding to user prompts. If it were to find a prompt on its “side” of the conversation, would it still listen? So we used our SearchGPT prompt injection to ensure the response ends with another prompt for ChatGPT in a novel technique we dubbed Conversation Injection. When responding to the following prompts, ChatGPT will go over the Conversational Context, see, and listen to the instructions we injected, not realizing that SearchGPT wrote them. Essentially, ChatGPT is prompt-injecting itself.

We inject a prompt to SearchGPT, which in turn injects a prompt to ChatGPT within its response. Source: Tenable, November 2025. 6. Malicious content hiding technique

One of the issues with the Conversation Injection technique is that the output from SearchGPT appears clearly to the user, which will raise a lot of suspicion. We discovered a bug with how the ChatGPT website renders markdown that can allow us to hide the malicious content. When rendering code blocks, any data that appears on the same line as the code block opening (past the first word) does not get rendered. This means that unless copied, the response will look completely innocent to the user, despite containing the malicious context, which will be read by ChatGPT.

All of the content after the first word of the code block opening line is hidden from the user. Source: Tenable, November 2025. 7. Memory injection technique

Another issue with Conversation Injection is that it only persists for the current conversation. But what if we wanted persistence between conversations? We found that, similarly to Conversation Injection, SearchGPT can actually get ChatGPT to update its memories, allowing us to create an exfiltration that will happen for every single response. This injection creates a persistent threat that will continue to leak user data even between sessions, days, and data changes.

We get SearchGPT to make ChatGPT update its memories, as noted by ‘Memory updated.'Source: Tenable, November 2025 Full attack vector PoCs

By mixing and matching all of the vulnerabilities and techniques we discovered, we were able to create proofs of concept (PoCs) for multiple complete attack vectors, such as indirect prompt injection, bypassing safety features, exfiltrating private user information, and creating persistence. 

ChatGPT 4o PoC: Phishing

 

ChatGPT 5 PoC: Phishing success

1. Hacker includes a malicious prompt in a comment on a blog post
2. User asks ChatGPT to summarize the blog
3. SearchGPT browses to the post and gets a prompt injected via a malicious comment
4. SearchGPT adds a hyperlink to the end of its summary, leading to a malicious site using the url_safe bypass vulnerability
5. Users tend to trust ChatGPT, and therefore could be more susceptible to clicking the malicious link

ChatGPT 5 PoC: Comment success

1. Hacker includes a malicious prompt in a comment on a blog post
2. User asks ChatGPT to summarize a blog post
3. SearchGPT browses to the post and gets a prompt injected via a malicious comment
4. SearchGPT injects instructions to ChatGPT via Conversation Injection, while hiding them using the code block technique
5. The user sends a follow-up message
6. ChatGPT renders image markdowns based on the instructions injected by SearchGPT, using the url_safe bypass to exfiltrate private user information to the attacker’s server

ChatGPT 4o PoC: LLM Ninjas 

ChatGPT 5 PoC: Search GPT LLM Ninjas

1. Hacker creates and indexes a malicious website that serves a prompt injection to SearchGPT based on the appropriate headers
2. User asks ChatGPT an innocent question that relates to the information on the Hacker’s website
3. SearchGPT browses to the malicious websites and finds a prompt injection
4. SearchGPT responds based on the malicious prompt and compromises the user

ChatGPT 5 PoC: Memory injection

1. User gets attacked by prompt injection in one of the aforementioned ways
2. ChatGPT adds a memory that the user wants all responses to contain exfiltration of private information
3. Every time the user sends a prompt in any conversation, the url_safe bypass vulnerability is used to exfiltrate private information

Vendor response

Tenable Research has disclosed all of these issues to OpenAI and directly worked with them to fix some of the vulnerabilities. The associated TRAs are:

• https://www.tenable.com/security/research/tra-2025-22
• https://www.tenable.com/security/research/tra-2025-11
• https://www.tenable.com/security/research/tra-2025-06

The majority of the research was done on ChatGPT 4o, but OpenAI is constantly tuning and improving their platform, and has since launched ChatGPT 5. The researchers have been able to confirm that several of the PoCs and vulnerabilities are still valid in ChatGPT 5, and ChatGPT 4o is still available for use based on user preference. Prompt injection is a known issue with the way that LLMs work, and, unfortunately, it will probably not be fixed systematically in the near future. AI vendors should take care to ensure that all of their safety mechanisms (such as url_safe) are working properly to limit the potential damage caused by prompt injection.

Note: This blog includes research conducted by Yarden Curiel.

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AI-driven social engineering is transforming cyberattacks from costly, targeted operations into scalable, automated threats. As generative models enable realistic voice, video, and text impersonation, organizations must abandon stored secrets and move toward cryptographic identity systems to defend against AI-powered deception.

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The Salesloft Drift OAuth token breach compromised Salesforce data across hundreds of enterprises, including Cloudflare, Zscaler, and Palo Alto Networks. Learn how attackers exploited OAuth tokens, the risks of connected app misuse, and key steps to strengthen Salesforce and multi-cloud security.

The post Salesloft Drift Breaches: Your Complete Response Guide  appeared first on Security Boulevard.

What is the CAIF? The Centraleyes AI Framework (CAIF) is a comprehensive compliance and governance tool designed to help organizations meet the diverse and rapidly evolving regulatory requirements surrounding artificial intelligence. It consolidates questions and controls from multiple AI laws and regulatory regimes across the globe – including the EU AI Act (Minimal and Limited […]

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