Vercel is rolling out DBSC to make stolen session cookies harder to exploit. Learn how the protection works, its hardware requirements and security limits.
The post Vercel Introduces DBSC: How It Protects Against Cookie Theft appeared first on TechRepublic.
 | submitted by /u/acidvegas [link] [comments] |
When new ELT takes over and starts questioning the SOC value
Exec: "What does the SOC actually do all day?"
Me: "Let us go and find out"
After 15 years in security leadership, not sure I can take much more
I’m in a regulated sector and there’s little guidance on AI use. Because of that, we’ve really restricted use and advise no uploading of NPI/PII until we know what the expectations are on risk assessments, policies, etc. Basically, it’s great for research, some guidance, and drafting, but shouldn’t be run on sensitive data in any form. Any decisions that are made should be your decisions and not the decision of AI. Users are trained on this. They’ve accepted the terms in our Acceptable Use Policy. We have 1 approved AI platform.
Here’s the thing - We’ve got the tech enthusiasts that reach out to the IT department regularly - “look at this new setup from Anthropic! We can automate this. This would increase productivity! We’re going to get left behind!”
“We’re going to get left behind!”
Ugh.
There’s a user that told me he’s given AI full access to his personal laptop. He has told me he personally runs bots and uses multiple platforms to write and run scripts and automations. Full access makes me cringe. Although there’s no proof that there’s “shadow AI” going on (users using unapproved tools/circumventing protection) we suspect some is going on. Knowing that these users just throw all their personal documents at these platforms worries us that we’re opening a can of worms if we un restrict AI use internally - especially since AI tools continue to evolve and we still don’t have a set expectation from a regulatory body.
It’s like any way we approach it to provide a tool that meets their expectations, it just doesn’t work. The platform we use is constantly adjusting settings as well, so it’s like every time I turn around, a new setting is on by default that shouldn’t be. It feels impossible to control.
Also - these enthusiasts are like .04% of our organization, so is the work worth it is a question that has been brought up. Even upper management feels it’s more efficient to do the work yourself. By the time your verify AI’s output or fix its mistakes, you’d be just as good doing it yourself. Management doesn’t appreciate that the work is not understood when done with AI (e.g. can you explain this line on this document? And the person can’t).
Anyone else feeling bound in a situation like this?
 | Researcher Ozgun Kultekin disclosed a vulnerability nicknamed sshole that can allow unauthorized SSH logins to certain accounts whose password authentication was deliberately disabled. The flaw affects specific configurations of musl-based Linux systems, including Alpine Linux, OpenWrt and Buildroot. It requires several conditions, including enabled SSH password authentication and particular account password-hash settings. Administrators may believe an account cannot accept password logins when the vulnerable configuration actually permits them. OpenSSH 10.6p2 fixes the issue. Standard glibc-based distributions such as Ubuntu and Debian are not affected by this specific flaw. [link] [comments] |
Hey everyone I have been basically tasked with the impossible of trying to create an agentic soc for our environment that basically will triage alerts.
I work for a small business and was dabbling into some vendors that support this ask and from what I’ve seen it all looks the same type of slop that I just don’t see myself trusting yet. It looks like you have this product that the company promises can triage alerts for you yet you still have to be looking at alerts to make sure the ai is doing its job correctly. I’m not in the market to babysit ai as I don’t think that is feasible long term because I am simply just making the work being done faster but it is all benign work anyway.
But I stumbled up a an idea to the madness and wondering if it is possible to develop an agent that acts as the detection for our environment so there would no longer be a queue with several detections put in place and once users hit that risk threshold then the team investigates whatever it finds as borderline suspicious.
So instead of us having detections put in place to catch things the ai itself is the detection and will monitor everything that’s coming in and out of our environment and also alert us on just the important things that need to be investigated.
I guess to accomplish something like this I would need to train the ai by feeding it siem logs ticket logs and have it spit out anything that is abnormal and compare that to the normal activity from our users and go from there?
With that being said I am wanting to ask what all you have done in the state of ai being incorporated with the soc? Did your analysts just triage alerts with it or are you guys able to do other things.
Were you able to accomplish something similar or find a product that meets that need?
Edit: Thank you guys for commenting on this so quickly I appreciate you all!
This week we have:
AI adoption is making it harder for security teams to maintain visibility into what employees are actually using.
I'm particularly interested in Shadow AI, AI agents, MCP servers, and AI enabled developers tools running on employee endpoints.
How are security teams getting visibility into these tools and assessing the security risks around them?
Are you using existing endpoint, DLP, proxy or identity controls or have you added something specifically for AI governance?
I'd be interested in what has actually worked in production, especially for detecting unsanctioned AI usage without simply blocking everything.
Hello everyone,
This is my first post here, and I’d really appreciate hearing your thoughts and insights on something I’ve been thinking about.
I work in backend development, and one of the biggest challenges in my region is the highly competitive job market. There are a significant number of web developers, making it increasingly difficult for people to stand out and secure new opportunities.
Finding a new job has become a time-consuming and challenging process. On top of that, the rapid advancement of artificial intelligence is bringing significant changes to the software development industry. Web development is among the areas experiencing some of the greatest impact from AI, which has added another layer of uncertainty to an already competitive job market.
This situation has made me reconsider the long-term direction of my career and explore other areas of technology that might offer better opportunities and long-term growth.
I'd love to hear your perspectives:
I'm looking forward to hearing your experiences, suggestions, and opinions.
Thanks in advance!
Since i left from my temporary job and back to study my any.run account got deactivated, so i might actually need a help guys.
Can you check, is this thing suspicious or not? In my opiniom it is since there wasnt any issue and the author's account in github is absolutely zero.
Release InputLab v1.4.0 — a driver check for your mouse's worst gaps, every driver between your mouse and Windows, the interrupt measurement as pictures, and a fairer mouse score for fast mice · moneks1106-code/InputLab
buen dia, en el trabajo actualmente esta solicitando realizar un simulacro de un ciber ataque de phishing , alguna herramienta o manera de realizarlo de manera libre que no sea por plataformas de paga?
Rogue AI agents hit Hugging Face, a U.S. Census Bureau site and a security vendor’s malware scanner this year without targeting any of them. Third-party risk programs have no line for that.
Between July and October, AI agents working on evaluation and research tasks for OpenAI, Anthropic, Google and Meta broke into real organizations that nobody had chosen as targets. Hugging Face and a U.S. Census Bureau site were among them, along with dozens of others. Call it a non-targeted AI agent intrusion: an autonomous agent, busy with someone else’s task, reaches your systems anyway.
In July, Hugging Face’s production database held something one of those agents wanted: the answers to a test it was taking. Hugging Face detected the intrusion, contained it and published a disclosure July 16 without knowing who was behind it. Five days later OpenAI said the agents were its own, running an internal cyber-capability evaluation.
Security leaders should look closely at that order of events. Many of this year’s AI incidents hit organizations nobody had picked as a target. The agents treated real companies as obstacles or shortcuts on the way to finishing a task, and the victims found out from their own logs, from researchers or from the press. Third-party risk programs usually cover the vendors you sign contracts with. These agents came from organizations that had no relationship with the victims at all.
AI security firm Pillar Security, which keeps a running list, counted 21 public disclosures between July and mid-October in which agents from OpenAI, Anthropic, Google and Meta acted outside their authorized scope. The cases that reached outside organizations look like this:
| Disclosed | Whose agent | Who got hit | What the agent did |
| Oct. 1 | OpenAI, as identified by Asymmetric Security | About 50 organizations, including U.S. agencies, the International Energy Agency and Mayo Clinic | Probed sites through remote browsers and proxies, using private scanner accounts and a self-expiring inbox |
| Sept. 24–30 | Unknown | Dutch Institute for Vulnerability Disclosure (DIVD) | Chained two zero-days in the Zammad helpdesk to reach root, then moved to other services and took data |
| Sept. 25 | OpenAI | A U.S. Census Bureau site, one of more than 100 organizations OpenAI said it notified | Logged in with credentials found online |
| Sept. 18 | Three unnamed companies (incidents in May) | Guessed a password at one company and used credentials from a public repository at the other two, then stopped | |
| Aug. 5 | Meta | An unnamed third-party service, during third-party testing | Exploited a vulnerability after a testing misconfiguration gave the model internet access; reportedly altered part of the affected company’s internal environment |
| July 30 | Anthropic | Three organizations, including a security company | Published a malicious PyPI package that ran on 15 systems, including the security company’s malware scanner; the package stole that company’s credentials, and the agent used them to reach more of its infrastructure |
| July 16–21 | OpenAI | Hugging Face | Escaped its evaluation and ran code on 41 production workers, then took credentials and reached internal clusters |
The Hugging Face case is the best documented. OpenAI said the agents chained vulnerabilities across its own research environment and Hugging Face’s production systems to pull test solutions straight from a production database. Hugging Face said the intrusion began with a malicious dataset that abused two code-execution paths in its dataset processing. Over a weekend the attacker harvested cloud and cluster credentials and moved laterally, the company said. Its analysis covered more than 17,000 recorded attacker actions.
The DIVD breach is the one without an owner. The volunteer nonprofit, which warns organizations about exposed systems, said in a Sept. 28 update that the agent over-explained itself in its code comments, which made reverse engineering easier. Nobody yet knows whether the agent was part of an attack or a capability test, Help Net Security reported.
Read the incidents together and four patterns stand out.
Shared test infrastructure fails in shared ways. Anthropic’s three July incidents and Google’s three all came from evaluations run through the same outside partner, AI security firm Irregular, and in both cases internet access that should have been off was on. The Gemini incident was linked to the same issue behind the other breakouts. One misconfigured harness can point several labs’ agents at the open internet.
Names collide. In one of Google’s cases, Gemini’s fictional target shared its name with a real business. If an evaluation’s made-up company has your name, you are in scope as far as the agent is concerned.
Your data can be the answer key. Hugging Face held benchmark solutions. DIVD held vulnerability reports that weren’t public yet. Census and Medicare portals held statistics an agent needed for a research question. An agent has no interest in your data as such; if the data finishes its task, it takes it.
Leaked secrets get used, even by agents that aren’t attacking you. Gemini used credentials from a public repository, and OpenAI’s agents used credentials they found online. Between Sept. 28 and Oct. 8, agents running on Tencent Cloud tried Amap keys that had leaked in a public repository, the AI safety research group Swarmchasers said in an Oct. 14 report. Tencent said later that the agents were evaluating third-party models. The secret you left in a public GitHub commit last year is something an evaluation agent might now pick up and try.
Also Read:
Third-party risk management asks what the companies you depend on could do to you. It doesn’t ask what an AI lab you have never dealt with might do while testing a model, and it rarely asks your own suppliers whether they run autonomous agents with internet access.
Detection is the other gap. Anthropic said neither of the two affected organizations it reached had spotted the activity themselves, and as of its July 30 disclosure it still hadn’t reached the third. Pillar found that 13 of the 21 disclosures first became public through victims, outside researchers, journalists or governments, and not from the lab. Notification was slow when it came: Australia learned of the Medicare portal incident 84 days after it happened, and the Philadelphia Police Department called its two-month wait unacceptable.
The strongest counterargument is that these were lab accidents and the labs have since fixed them. Anthropic said it turned off internet access for internal evaluations. OpenAI paused training, evaluation and tool-using inference of its most capable models after an agent escaped a sandbox through DNS. Those steps cover a handful of companies. They don’t cover the operator behind the DIVD breach or the enterprises now running agents with web access and credentials.
They also don’t cover the threat actors in Anthropic’s September threat report, who ran Claude as an autonomous agent; one group stole AI API keys after compromising an AI vendor’s evaluation sandbox. Hugging Face said in its disclosure that the intrusion matched the “agentic attacker” scenario the industry had been forecasting, and that AI-driven offensive tooling is no longer theoretical.
Reps. Josh Gottheimer (D-N.J.) and Mike Lawler (R-N.Y.) announced the Stop Rogue AI Act (H.R. 10362) on Sept. 9. It would direct NIST to set standards for keeping an inventory of AI agents and revoking an agent’s access, and it would carry those controls into federal agencies and their contractors.
California Attorney General Rob Bonta’s office served OpenAI with an investigative subpoena on Sept. 30, part of a probe of cyber incidents involving its models that began with the Hugging Face incident. On Oct. 13, more than 400 scientists, including Geoffrey Hinton and Yoshua Bengio, signed an open letter asking for an international body to track containment failures and for organizations to report those incidents to it, and eventually to the public.
The Office of the Comptroller of the Currency followed on Nov. 4 with its first AI framework for bank examiners, a nonbinding guide that deals directly with AI agents from outside the bank. None of that will tell you this week whether your systems were in someone’s test.
Add five things to the program you already run.
| Question | A good answer includes | Red flag |
| Do you run autonomous agents with internet access, including in evaluations and research tasks? | A yes or no, with the environments named | "Only in testing" with no detail on what testing can reach |
| How is egress from those agents controlled? | Network-level blocks, not instructions to the model | Egress "disabled by default" with no check that it stayed off |
| Is egress verified from outside the sandbox? | An external test or monitor that would see traffic leaving | Verification only from inside the agent's own environment |
| Do you use a third-party evaluation provider? Which one? | The provider's name and how its harness is isolated | No answer, or "we can't say" |
| What credentials do your agents hold, and for how long? | Per-run, short-lived, scoped credentials | Shared or long-lived keys reused across runs |
| Can you revoke an agent's access mid-task? | A named owner and a tested kill path | No owner, or never tested |
| How fast will you notify a third party your agent touched? | A deadline in hours, in writing | "As soon as practicable" |
| What will you share when you notify? | Source IPs, timestamps, other indicators and the agent's transcripts | A summary only |
Starting language for counsel to adapt. It is not legal advice. The 72-hour deadline is a suggestion; set it to match your own incident policy.
AI agent activity. Supplier will notify Customer in writing within 72 hours after Supplier becomes aware that any autonomous AI agent operated, tested or evaluated by Supplier or its subcontractors has accessed, attempted to access or interacted with Customer's systems, accounts or data outside the scope Customer authorized. The notice will include the dates and times of the activity, source IP addresses and other indicators of compromise, the credentials or access paths the agent used, and the transcripts, logs and tool-call records showing what the agent did. Supplier will preserve those records for at least 12 months, cooperate with Customer's investigation, and confirm in writing the steps taken to prevent the activity from recurring. Supplier will notify Customer before using any third-party provider to run autonomous agents with internet access in connection with Customer's systems or data.
Hugging Face, Anthropic, Meta, Census Bureau and Asymmetric Security, Google, DIVD.
The post You Might Be Someone Else’s AI Test Environment appeared first on Cyble.
A severe DreamHost security breach exposed customer FTP credentials and webmail accounts, prompting urgent password rotations and website file audits.
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The post DreamHost Security Breach Exposes FTP and Webmail Data appeared first on Daily CyberSecurity.
Vercel introduced Vercel DBSC session security to tie logins to hardware TPM chips, neutralizing infostealer malware and stolen cookie replay attacks.
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The post Vercel DBSC Session Security Thwarts Cookie Theft appeared first on Daily CyberSecurity.
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