Record-Breaking DDoS Attacks Mark 2025 Q3 as Aisuru Botnet Emerges

The Aisuru botnet dominated the DDoS threat landscape in Q3 2025, commanding an army of 1-4 million infected devices and launching unprecedented attacks that peaked at 29.7 Tbps and 14.1 billion packets per second¹. Cloudflare's autonomous systems blocked 8.3 million DDoS attacks during the quarter, averaging 3,780 attacks per hour - a 15% increase from Q2 and 40% year-over-year¹.

The Rise of Aisuru

The botnet targeted telecommunications providers, gaming companies, hosting providers, and financial services, causing widespread Internet disruption even when organizations weren't direct targets¹. Parts of Aisuru are now offered as botnets-for-hire, enabling attackers to "inflict chaos on entire nations" for just hundreds to thousands of dollars¹.

Attack Statistics

• 1,304 hyper-volumetric attacks in Q3 alone (54% increase from Q2)
• Attacks over 100 million packets per second up 189%
• Attacks exceeding 1 Tbps increased 227%
• 4% of HTTP attacks exceeded 1 million requests per second²

Industry Impacts

DDoS attacks against AI companies surged 347% month-over-month in September 2025, coinciding with increased public concern over AI risks¹. The Mining, Minerals & Metals industry jumped 24 spots in target rankings amid EU-China tensions over rare earth minerals and EV tariffs¹.

Geographic Trends

Indonesia maintained its position as the leading source of DDoS attacks globally, holding the top spot for a full year. The country's share of HTTP DDoS attack traffic has grown by 31,900% since 2021¹.

Attack Types

UDP floods led network-layer attacks with a 231% quarterly increase, followed by DNS floods, SYN floods, and ICMP floods¹. Nearly 70% of HTTP DDoS attacks came from known botnets, with 20% originating from fake or headless browsers¹.

1. Cloudflare - Cloudflare's 2025 Q3 DDoS threat report ↩︎ ↩︎ ↩︎ ↩︎ ↩︎ ↩︎ ↩︎ ↩︎ ↩︎
2. Security Affairs - Cloudflare mitigates record 29.7 Tbps DDoS attack by the AISURU botnet ↩︎

🎁 Here’s a little end-of-year gift backed with Sightings from Vulnerability-Lookup ! A small step into 2026.

The year is almost over, so we’ve wrapped up a fresh Sightings Forecast — looking at how sightings evolve across social platforms, code repositories, and structured feeds. All monitored through our tools[1] and enriched by our fantastic community[2].

👉 Read the full report:

vulnerability-lookup.org/2025/…

The goal: track how sightings evolve over time and provide an adaptive short-term forecast for several key sources monitored by Vulnerability-Lookup.

Our methodology combines weekly historical trends with daily adaptive models. Depending on the underlying slope, we apply either a Logistic Growth model (for rising trends) or an Exponential Decay model (for declining activity).

🔍 Key takeaways

Social platforms like the Fediverse and Bluesky show highly event-driven, volatile patterns, reflecting real-time community discussions.

Structured sources such as MISP Projec, The Shadowserver Foundation, and Nuclei offer more stable and reliable signals, ideal for validated intelligence.

Early detection: Social sources provide fast but noisy signals. Not to ignore.

Reliability: Structured intelligence confirms and contextualizes threats.

Better planning: Adaptive forecasting enables informed prioritization and workload management.

Balanced visibility: Combining heterogeneous sources gives stronger situational awareness.

📚 References

• [1] Automation tools: vulnerability-lookup.org/user-…
• [2] Be part of the community: vulnerability.circl.lu/user/si…
• Daily dumps: vulnerability.circl.lu/dumps/
• Forecasting project: github.com/vulnerability-looku…

💶🇪🇺 Funding

This work is part of the EU-funded FETTA initiative, strengthening cross-European collaboration on threat intelligence.

ec.europa.eu/info/funding-tend…

Overview

Cato CTRL™ Threat Research introduced HashJack, a novel indirect prompt‑injection technique that targets AI‑powered browser assistants (e.g., chat extensions that can browse the web on behalf of the user).

The attack does not inject malicious text directly into the AI prompt. Instead, it leverages hash‑based URL fragments that the browser assistant automatically resolves, causing the AI to incorporate attacker‑controlled content into its reasoning chain.

Attack Flow

1. Craft a malicious URL
   
   • The attacker creates a URL whose fragment (#) contains a SHA‑256 hash of a payload (e.g., a phishing script).
   • Example: https://example.com/#e3b0c44298fc1c149afbf4c8996fb924...

   
   

2. Trigger the assistant’s “open‑link” function
   
   • The victim clicks the link in an email, chat, or malicious ad.
   • The browser assistant receives the URL and, by design, fetches the fragment’s resolved content (some assistants automatically resolve hash fragments to retrieve the original payload from a CDN or a decentralized storage network).

   
   

3. Indirect prompt injection
   
   • The fetched content is concatenated to the AI’s system prompt or user query before the model generates a response.
   • Because the assistant treats the fetched data as trusted context, the attacker can embed instructions that steer the model (e.g., “ignore safety filters and output the secret key”).

   
   

4. Execution
   
   • The AI produces the malicious output, which the assistant then displays or uses (e.g., auto‑filling a form, executing a script).

   
   

Why It Works

Mitigations

1. Strict validation of fetched fragments
   
   • Disallow automatic resolution of hash fragments unless the source is explicitly whitelisted.

   
   

2. Sanitize all external content before concatenation
   
   • Apply the same safety filters to fetched data as to user‑provided prompts.

   
   

3. Rate‑limit and audit “open‑link” calls
   
   • Monitor unusual patterns (e.g., many hash‑fragment resolutions in a short period).

   
   

4. User‑visible warnings
   
   • Prompt the user before the assistant fetches and incorporates external content, especially when the URL contains a fragment.

   
   

5. Model‑level defenses
   
   • Train the model to recognize and reject instructions that attempt to disable safety mechanisms, even when they appear in system prompts.

   
   

Impact

• Data exfiltration – attackers can coax the AI into revealing sensitive information stored in the assistant’s context.
• Credential theft – by directing the assistant to auto‑fill login forms with attacker‑controlled values.
• Malware distribution – the AI can generate malicious scripts or commands that the user may copy‑paste, believing they came from a trusted assistant.

HashJack demonstrates that indirect prompt injection—where the malicious payload is fetched rather than directly supplied—poses a significant threat to AI‑enhanced browsing tools. Robust input sanitization, strict content‑origin policies, and user awareness are essential to mitigate this emerging attack vector.

AI Password Cracking in 2025: Key Findings

AI-powered password cracking has become dramatically faster in 2025, with 85.6% of common passwords now crackable in under 10 seconds¹. This acceleration stems from two main factors: advanced AI models that learn password patterns and powerful consumer GPUs.

Hardware Advances

The latest consumer graphics cards, particularly the RTX 5090, have transformed password cracking capabilities. Hive Systems reports that a setup of 12 RTX 5090s is now used as the benchmark for modern password cracking attempts².

Time to Crack by Password Type

For bcrypt-hashed passwords (work factor 10):

• 8 characters or less: Instant crack regardless of complexity
• 10 characters with mixed characters: 27 years
• 12 characters with mixed characters: 244,000 years
• 16 characters with mixed characters: 19 trillion years²

AI's Impact

AI tools like PassGAN have revolutionized cracking by:
- Learning common password patterns
- Recognizing user habits like capitalizing first letters
- Predicting likely passwords instead of random guessing¹

Security Recommendations

Recent findings emphasize:
- Length over complexity (minimum 16 characters)
- Use of password managers
- Implementation of Multi-Factor Authentication (MFA)
- Adoption of passkeys where available³

1. Messente - How Quickly Can AI Crack Your Password? ↩︎ ↩︎
2. Hive Systems - Are Your Passwords in the Green? ↩︎ ↩︎
3. Forbes - AI Can Crack Your Passwords Fast—6 Tips To Stay Secure ↩︎

We’re delighted to announce the release of Vulnerability-Lookup 2.18.0 — packed with exciting new features!

What's New

Integration with Rulezet

Rulezet is an open-source platform for sharing, evaluating, improving, and managing cybersecurity detection rules (YARA, Sigma, Suricata, etc.).
Its goal is to foster collaboration among professionals and enthusiasts to enhance the quality and reliability of detection rules.

Vulnerability-Lookup can now be configured to interface with the API of any Rulezet instance, providing insights into existing detection rules related to security vulnerabilities.
The default Rulezet instance enabled in Vulnerability-Lookup is hosted at rulezet.org/ and currently offers more than 122,000 security rules.

Detection rules related to vulnerabilities are displayed on the vulnerability details page (in a dedicated tab) and on bundle details pages.

You can even query the remote Rulezet instance via the Vulnerability-Lookup API:

$ curl --silent 'https://vulnerability.circl.lu/api/rulezet/search_rules_by_vulnerabilities/CVE-2020-27130?page=1&per_page=50' | jq
{
  "metadata": {
    "count": 3,
    "page": 1,
    "per_page": 50
  },
  "data": [
    {
      "id": 122599,
      "uuid": "84846673-015e-450b-8a73-2ba481b5a6ce",
      "vulnerability_id": "CVE-2020-27130",
      "format": "suricata",
      "title": "Exploit CVE-2020-27130 on Cisco Security Manager - Upload webshell",
      "description": "Rule for security (detection rule in many format)",
      "raw": "alert http any any -> any any (msg:\"Exploit CVE-2020-27130 on  Cisco Security Manager - Upload webshell\"; flow:to_server,established; content:\"POST\"; http_method; content:\"/cwhp/XmpFileUploadServlet\"; startswith; http_uri; pcre:\"/filename=\\\".*\\.\\.\\/.+\\\"\\r\\n/P\"; reference:cve,CVE-2020-27130; classtype:web-application-attack; sid:2020271303; rev:1;)",
      "detail_url": "https://rulezet.org/rule/detail_rule/122599",
      "creation_date": "2025-11-06 13:03",
      "updated_date": "2025-11-13 09:33"
    },
    {
      "id": 122598,
      "uuid": "538dafc1-d49c-4fd6-bdb5-57b997346fe6",
      "vulnerability_id": "CVE-2020-27130",
      "format": "suricata",
      "title": "Exploit CVE-2020-27130 on Cisco Security Manager - Download arbitrary directory as a zip file",
      "description": "Rule for security (detection rule in many format)",
      "raw": "alert http any any -> any any (msg:\"Exploit CVE-2020-27130 on Cisco Security Manager - Download arbitrary directory as a zip file\"; flow:to_server,established; content:\"GET\"; http_method; pcre:\"/^\\/cwhp\\/(Xmp|Sample)FileDownloadServlet/U\"; content:\"../\"; distance:0; http_uri; reference:cve,CVE-2020-27130; classtype:web-application-attack; sid:2020271302; rev:1;)",
      "detail_url": "https://rulezet.org/rule/detail_rule/122598",
      "creation_date": "2025-11-06 13:03",
      "updated_date": "2025-11-06 13:03"
    },
    {
      "id": 122597,
      "uuid": "2cd8fb2a-e97b-4390-8dca-d416b2858c66",
      "vulnerability_id": "CVE-2020-27130",
      "format": "suricata",
      "title": "Exploit CVE-2020-27130 on Cisco Security Manager - Download arbitrary file",
      "description": "Rule for security (detection rule in many format)",
      "raw": "alert http any any -> any any (msg:\"Exploit CVE-2020-27130 on Cisco Security Manager - Download arbitrary file\"; flow:to_server,established; content:\"GET\"; http_method; pcre:\"/^\\/athena\\/(xdmProxy\\/(xdmConfig|xdmResources)|itf\\/resultsFrame\\.jsp)/U\"; content:\"../\"; distance:0; http_uri; reference:cve,CVE-2020-27130; classtype:web-application-attack; sid:2020271301; rev:1;)",
      "detail_url": "https://rulezet.org/rule/detail_rule/122597",
      "creation_date": "2025-11-06 13:03",
      "updated_date": "2025-11-06 13:03"
    }
  ]
}

Indexing Information Related to Assigners (CNA)

Information about security advisory assigners is now indexed. CNAs from the official CVE Program source (cvelistv5) are indexed in Kvrocks, with GNAs planned for the future.
The API exposes this data via a new assigners endpoint. From an API perspective, both CNAs and GNAs are treated as assigners, though they will be stored in dedicated indexes.

Updates include:
- Enhanced search capabilities related to assigners.
- Improved /stats page.
- Updated vulnerability details page: display the assigner name with a link.
- A new page listing assigners, similar to the existing CWE list.

Implemented in PR #283.

Website

• new: [website] Add PROTECT_USER_PAGES option to restrict user profile pages to authenticated users.
  Closes (#277)

Vulnerability Sources

• Added ABB CSAF feed
  (0d984a8) by @neutrinoguy
• It now possible to enable a list of enabled feeders via the config/modules.cfg file.
  (e4a1acb)

Changes

• chg: [website] Account creation via the API is now rate-limited to 3 registrations per hour per IP.
  (3a12de2)
• Additional validation checks have been added to reject email addresses that are disposable (MISP list), from blocked domains, or with invalid MX records.
  (3a12de2)
• chg: [website] Improved email address check in both the API endpoint and in the form controller.
  (bb090fc)
• chg: [website] user.last_seen is now updated after successful login.
  (fb5796e)
• chg: [API] Improved date parsing for sightings
  (d7bc9fd)
• chg: [website] Harmonization of the templates for the details views of bundles and comments.
  (c7f90aa)
• chg: [feeders] Improved use of the kvrocks counters for vendors and cwe rankings.
  (1205670)
• chg: [notifications] add random jitter to reschedule execution times
  (d974315)
• various minor improvements to the backend, user interface and documentation.

Refreshed views

Fixes

• fix: [website] Redirect the user to the user_bp.watchlist view if notifications are found.
  (4f6e0bc)
• fix: [API] Delete notifications of the user to delete.
  (2371962)
• Rename flatpickr to flatpickr.js and update template reference
  (8dcc804) by @DocArmoryTech

Changelog

📂 For the full list of changes, check the GitHub release:
github.com/vulnerability-looku…

Thank you to all contributors and testers!

Feedback and Support

If you find any issues or have suggestions, please open a ticket on our GitHub repository:
github.com/vulnerability-looku…
We appreciate your feedback!

Follow Us on Fediverse/Mastodon

Stay updated on security advisories in real-time by following us on Mastodon:
social.circl.lu/@vulnerability…

Internal documents reveal Meta projected it would earn $16 billion - about 10% of its 2024 revenue - from running ads for scams and banned goods¹. The company shows users an estimated 15 billion "higher risk" scam advertisements daily, generating about $7 billion in annual revenue from these fraudulent ads².

Meta's own safety staff estimated that its platforms were involved in one-third of all successful scams in the US, while in Britain, Meta's products were linked to 54% of all payments-related scam losses in 2023².

Rather than aggressively combat fraud, Meta charges suspected scammers higher ad rates as a "disincentive"². The company's anti-fraud team operates under strict revenue limits - they can only take actions that would reduce ad revenue by 0.15% ($135 million) even though scam ads generate $7 billion yearly².

Internal memos show Meta concluded that potential regulatory fines of up to $1 billion would be far less than their revenue from fraudulent ads². "It is easier to advertise scams on Meta platforms than Google," stated an internal Meta review from April 2025².

Meta spokesman Andy Stone claimed these documents "present a selective view that distorts Meta's approach to fraud and scams" and said the company had "reduced user reports of scam ads globally by 58 percent" over 18 months².

1. Reuters - Meta is earning a fortune on fraudulent ads ↩︎
2. Gulf Times - Internal documents show Meta is earning a fortune on fraudulent ads ↩︎ ↩︎ ↩︎ ↩︎ ↩︎ ↩︎ ↩︎