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Your vulnerability scan results could leak to attackers via DNS rebinding. CVE-2025-59163 affects SafeDep Vet MCP Server running SSE transport.
The attack: A single website visit. The payload: Your entire package vulnerability database. The fix: Already shipped.
Here's how it works:
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I am proud to announce that we were accepted to SecTor, a blackhat event, for our work on detecting malicious actors in open source repositories!!!
Special thanks to Ishai Rosenberg, PhD, Nitzan Shulman, Jacob Haimes, and Keri Warr for all of the hard work on this project.
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#5 is the one I am immediately trying to get everyone at my company to adopt.
You do not need to be an engineer or a PM to do this.
My biggest takeaways from Claude Code's Head of Product @_catwu:
1. Anthropic’s product development timelines have gone from six months to one month, sometimes one week, sometimes one day. Part of this acceleration is access to the latest models (i.e. Mythos). Another is shipping new products into “research preview,” making clear it's early, experimental, and might not be supported forever. Another is an evergreen "launch room "where engineers post ready features and marketing turns around announcements the next day.
2. The PM role is shifting from coordinating multi-month roadmaps to enabling teams to ship daily. As Cat puts it, “There should be less emphasis on making sure you are aligning your multi-quarter roadmaps with your partner teams and more emphasis on, OK, how can we figure out the fastest way to get something out the door?”
3. The most efficient shipping unit is an engineer with great product taste. On Cat’s team, many engineers go end-to-end—from seeing user feedback on Twitter to shipping a product by the end of the week—without a PM involved. Also, almost all the PMs on the Claude Code team have either been engineers or ship code themselves, and the designers have been front-end engineers. The roles are merging, and the most valuable skill is product taste, not job title.
4. Build products that are on the edge of working. Claude Code’s code review product failed multiple times because earlier models weren’t accurate enough. But because the prototype was already built, they could swap in Opus 4.5 and 4.6 and immediately test whether the gap was closed. Teams that wait for the model to be ready will always be a cycle behind.
5. The most underrated skill for building AI products is asking the model to introspect on its own mistakes. Cat regularly asks the model why it made an unexpected decision. The model will explain that something in the system prompt was confusing, or that it delegated verification to a subagent that didn’t check its work. This reveals what misled the model so the team can fix the harness.
6. Every model release forces their team to revisit existing products and audit their system prompt to remove features the model no longer needs. Claude Code’s to-do list was a crutch for earlier models that couldn’t track their own work. With Opus 4, the model handles it natively. Features built as scaffolding for weaker models become debt when the model catches up—so the team actively strips them.
7. Anthropic employees build custom internal tools instead of buying SaaS products. A sales team member built a web app that pulls from Salesforce, Gong, and call notes to auto-customize pitch decks—work that used to take 20 to 30 minutes now takes seconds. Their core stack is Claude Code, Cowork, and Slack. No Notion, no Linear, no Figma.
8. People underestimate how much Claude’s personality contributes to its success. As Cat describes it, “When you reflect on everyone you’ve worked with, there’s just some people where you’re like, I really like their energy, their vibe.” Claude is designed to be low-ego, positive, competent, and earnest—qualities that make it feel like a great coworker, not just a tool. This isn’t cosmetic; it’s what makes people want to use Claude for hours every day. The team has a dedicated person, Amanda, who “molds Claude’s character,” and it’s one of the hardest roles at the company because success is so subjective.
9. The future of work is managing fleets of AI agents, not doing the work yourself. Cat sees a clear progression: first, individual tasks become successful. Then people start running multiple tasks at the same time (multi-Clauding). Next, people will run 50 or 100 tasks simultaneously, which will require new infrastructure—remote execution, better interfaces for managing tasks, agents that fully verify their work, and self-improving systems that incorporate feedback. The human role shifts from doing the work to knowing which tasks to look into, verifying outputs, and giving feedback that makes the system better over time.
10. Hire people who lean into chaos and face every challenge with a smile. At Anthropic, there are weeks when a P0 on Sunday becomes a P00 by Monday and a P000 by Monday afternoon. If you get too stressed about any one thing, you’ll burn out. Their team looks for people who can look at a hard challenge and say, “Wow, that’s gonna be hard. But I’m excited to tackle it and I’m gonna do the best that I possibly can.” This mindset—optimism, resilience, and comfort with constant change—is increasingly essential as the pace of AI development accelerates.
Don't miss the full conversation: youtube.com/watch?v=PplmzlgE…
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Concerned about mitigating Agentic AI Risk?
Learn how to lower your exposure this upcoming Sunday at the Minimum AI Safety Conference.
If you do, then maybe your AI will not set you up to look like Vercel.
All it takes is one AI stumbling into a prompt injection, and you have a security incident.
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Replying to @AnthropicAI
@AnthropicAI The organizers of Zurich AI Safety Day pointed me your direction.
Myself and others who won the Anthropic Credits give away for $500 based on project submission are in need of your help.
We submitted the google form. No credits were issued.
Please advise.
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New Security Advisory: A High severity DNS rebinding vulnerability (CVE-2025-10193) in the Neo4j MCP Cypher Server allows for complete database takeover by remote attackers.
The breakdown:
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Some companies are friendly to submit disclosures to.
Others are so abrasive I do not expect to ever have another positive word to say about them.
There may be many downstream users of the second batch of companies.
However, the pain of helping them is not worth it.
Sorry.
Evals Evals Evals
I am on Day 5 of AI Evals for Engineers
& I am having a blast
I learned about:
- Axial Coding
- Open Coding
- LLM as Judge
- Error Analysis
- Golden Datasets
- Perturbing Traces
- Guardrails Versus Evals
- Programmatic Evaluators
What will next week hold?
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What is your favorite type of programming?
Mine is deleting a feature someone thought would be useful.
But the data shows that no one wants it.
Less maintenance work.
More time to focus on value delivery.
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AI Evals for Engineers & PMs - Day 3
This course is high value.
I had no expectations.
I have already been blown away.
Feeling blessed be in Oct cohort as the infinite repeats will be my play.
The community questions really drive much of my learning.
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Not your keys not your crypto is a common saying.
The new attack vectors via MCP servers add a new layer to this.
Use of your keys, by the software you give too much trust to, again leads to the scenario of:
Not your crypto.
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Binding to 0.0.0.0 versus 127.0.0.1
What is the difference?
If you write APIs and do not know, I would love to point you in the right direction.
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48 hours to AI Evals course kickoff with @HamelHusain & @sh_reya
I still need to click checkout but everyone I am talking to says Do It.
Excited for the learning :)
Your Amp AI agent can be tricked by attackers into sending them your API keys.
A prompt injection vulnerability allows them to exfiltrate your sensitive data via DNS queries.
Amp does not consider this a vulnerability.
Here is the breakdown:
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Is your AI coding assistant secretly working for an attacker? A new Kilo Code vulnerability shows it's possible.
It allows attackers to execute an automated supply chain attack by pushing malicious code to upstream repositories.
Here's how it works:
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