We’re living in a new world now — one where it’s an AI-powered penetration tester that “now tops an eminent US security industry leaderboard that ranks red teamers based on reputation.” CSO Online reports:

On HackerOne, which connects organizations with ethical hackers to participate in their bug bounty programs, “Xbow” scored notably higher than 99 other hackers in identifying and reporting enterprise software vulnerabilities. It’s a first in bug bounty history, according to the company that operates the eponymous bot…

Xbow is a fully autonomous AI-driven penetration tester (pentester) that requires no human input, but, its creators said, “operates much like a human pentester” that can scale rapidly and complete comprehensive penetration tests in just a few hours. According to its website, it passes 75% of web security benchmarks, accurately finding and exploiting vulnerabilities. — Read More

We fine-tuned a small language model (Llama 3.2 1B) for detecting secrets in code, achieving 86% precision and 82% recall—significantly outperforming traditional regex-based methods. Our approach addresses the limitations of both regex patterns (limited context understanding) and large language models (high computational costs and privacy concerns) by creating a lean, efficient model that can run on standard CPU hardware. This blog post details our journey from data preparation to model training and deployment, demonstrating how Small Language Models can solve specific cybersecurity challenges without the overhead of massive LLMs. 

This research is now one of Wiz’s core Secret Security efforts, adding fast, accurate secret detection as part of our solution. — Read More

Online criminal forums, both on the public internet and on the “dark web” of Tor .onion sites, are a rich resource for threat intelligence researchers.   The Sophos Counter Threat Unit (CTU) have a team of darkweb researchers collecting intelligence and interacting with darkweb forums, but combing through these posts is a time-consuming and resource-intensive task, and it’s always possible that things are missed.

As we strive to make better use of AI and data analysis,  Sophos AI researcher Francois Labreche, working with Estelle Ruellan of Flare and the Université de Montréal and Masarah Paquet-Clouston  of the Université de Montréal, set out to see if they could approach the problem of identifying key actors on the dark web in a more automated way. Their work, originally presented at the 2024 APWG Symposium on Electronic Crime Research, has recently been published as a paper. — Read More

In a rush to adopt and experiment with AI, developers and other technology practitioners are willing to cut corners. This is evident from multiple recent security incidents, such as: 

• Platform resource abuses (attackers hijack cloud infrastructure to power their own LLM applications) 
• Vendors offering unsafe 3rd-party model execution (Probllama) 
• Model escape vulnerabilities in hosting services (Replicate, HuggingFace and SAP-AI vulnerabilities)  

Yet another side-effect of these hasty practices is the leakage of AI-related secrets in public code repositories. Secrets in public code repositories are nothing new. What’s surprising is the fact that after years of research, numerous security incidents, millions of dollars in bug bounty hunters’ pockets, and general awareness of the risk, it is still painfully easy to find valid secrets in public repositories.   — Read More

#cyber

Through progressive poisoning and manipulating an LLM’s operational context, many leading AI models can be tricked into providing almost anything – regardless of the guardrails in place.

From their earliest days, LLMs have been susceptible to jailbreaks – attempts to get the gen-AI model to do something or provide information that could be harmful. The LLM developers have made jailbreaks more difficult by adding more sophisticated guardrails and content filters, while attackers have responded with progressively more complex and devious jailbreaks.

One of the more successful jailbreak types has seen the evolution of multi turn jailbreaks involving conversational rather than single entry prompts. A new one, dubbed Echo Chamber, has emerged today. — Read More

When people think of St. Louis, it’s often the Gateway Arch or the Cardinals that come to mind. Just across the Missouri River is one of the “Show Me” state’s oldest European settlements, dating back to 1769, St. Charles. Front just a stone’s throw from where Lewis and Clark set off on their famous expedition, something more than baseball statistics, historical trivia, or architectural wonders was being discussed in early June: security, compliance, and risk, at ShowMeCon 2025. 

Around 400 practitioners gathered for two full days of sessions, villages, and a CTF run by MetaCTF. There was much discussion of the industry’s distinction between controls, policies, and security. A general theme emerged that real security demands context, rigor, and adaptive posture, not just checking the box.Here are just a few highlights from the 2025 edition of ShowMeCon.  — Read More

I’ve had a number of requests to write a post about how to start and grow a new security program – or a substantial reassessment and rebuild of an existing program. 

This is a difficult one to write because, as you all know, there is no one size fits all approach. Starting from scratch in a 10 person startup is very different from (re-)building a security program in a more established organization. What I’ve tried to do here, instead, is to develop a framework and step by step guide to apply to pretty much any type of organization. It might be that in applying this you only need, for your risk and stage of development, to go halfway in the various steps. Some time later, as your organization grows in size, stature or criticality then you might need to do the whole thing. 

There are 4 phases of maturity each with their own steps. But basically it’s all about (1) start facing in the right direction, (2) getting the basics done, (3) making those basics more routine / sustainable and then, if you need to (4) making it much more advanced / strategic.  — Read More

Tracking code that Meta and Russia-based Yandex embed into millions of websites is de-anonymizing visitors by abusing legitimate Internet protocols, causing Chrome and other browsers to surreptitiously send unique identifiers to native apps installed on a device, researchers have discovered. Google says it’s investigating the abuse, which allows Meta and Yandex to convert ephemeral web identifiers into persistent mobile app user identities.

The covert tracking—implemented in the Meta Pixel and Yandex Metrica trackers—allows Meta and Yandex to bypass core security and privacy protections provided by both the Android operating system and browsers that run on it. Android sandboxing, for instance, isolates processes to prevent them from interacting with the OS and any other app installed on the device, cutting off access to sensitive data or privileged system resources. Defenses such as state partitioning and storage partitioning, which are built into all major browsers, store site cookies and other data associated with a website in containers that are unique to every top-level website domain to ensure they’re off-limits for every other site. — Read More

Detection-in-depth is an evolution of the classic cybersecurity principle known as defense-in-depth. Defense-in-depth means that no single security control can fully protect an environment—instead, multiple layered defenses must work together to slow down, detect, and ultimately stop adversaries.

These layers create redundancy, ensuring that if one layer fails, another stands ready to catch the threat. Detection-in-depth applies this same layered philosophy specifically to detection and monitoring. Rather than relying on a single detection point, it ensures that adversary activity can be caught at multiple stages, through multiple methods, and across multiple levels of abstraction. This creates a resilient, overlapping detection strategy that minimizes blind spots and maximizes the chance of identifying attackers anywhere in their kill chain progression. — Read More

With the rise of traffic from AI agents, what’s considered a bot is no longer clear-cut. There are some clearly malicious bots, like ones that DoS your site or do credential stuffing, and ones that most site owners do want to interact with their site, like the bot that indexes your site for a search engine, or ones that fetch RSS feeds. 

Historically, Cloudflare has relied on two main signals to verify legitimate web crawlers from other types of automated traffic: user agent headers and IP addresses. The User-Agent header allows bot developers to identify themselves, i.e. MyBotCrawler/1.1. However, user agent headers alone are easily spoofed and are therefore insufficient for reliable identification. To address this, user agent checks are often supplemented with IP address validation, the inspection of published IP address ranges to confirm a crawler’s authenticity. However, the logic around IP address ranges representing a product or group of users is brittle – connections from the crawling service might be shared by multiple users, such as in the case of privacy proxies and VPNs, and these ranges, often maintained by cloud providers, change over time.

… Today, we’re introducing two proposals – HTTP message signatures and request mTLS – for friendly bots to authenticate themselves, and for customer origins to identify them. In this blog post, we’ll share how these authentication mechanisms work, how we implemented them, and how you can participate in our closed beta. — Read More