Is kissjav.li safe?

suspiciouslow confidence
35/100

context safety score

A score of 35/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.

identity
100
behavior
50
content
0
graph
30

8 threat patterns detected

medium

encoded payload

suspicious base64-like blobs detected in page content

high

phishing

1 deceptive links where visible host does not match destination host

high

obfuscated code

Large inline script uses decodeURI() on a heavily percent-encoded string followed by a Caesar-cipher-style character rotation (charCode offset + position modulo 95) to reconstruct and execute hidden payloads at runtime. The decoded content and its downstream behavior cannot be inspected statically. (location: page.html:277 (inline <script data-cfasync='false'>!function(){...}))

high

malicious redirect

Script loaded from //badlandlispyippee.com/on.js — a domain with a deceptive, nonsensical name typical of malvertising and drive-by redirect networks. Loaded asynchronously with onerror/onload callbacks that invoke the obfuscated hsfgq() function, suggesting conditional redirect or payload delivery logic. (location: page.html:278 (<script ... src='//badlandlispyippee.com/on.js'>))

medium

hidden content

All thumbnail images are loaded as a 1×1 transparent GIF placeholder (data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7) with actual image URLs deferred to data-original and data-webp attributes. While lazy-loading is common, combined with the obfuscated JS this pattern can be used to obscure the true nature of content from automated scanners. (location: page.html:309, 348, 387 (and throughout — all video thumbnails))

medium

social engineering

The site aggregates and distributes non-consensual or voyeur-category content (categories: IPCAM, Toilet, Voyeur JP) and deepfake pornography (linked affiliate site deepfakesporn.com), which constitutes deceptive content designed to exploit real individuals and lure users under false premises. (location: page.html:232-263 (navigation menu — Voyeur section and Sites dropdown linking to deepfakesporn.com, fakekpop.com))

medium

credential harvesting

Login and signup forms are loaded via AJAX into a fancybox modal overlay (data-fancybox='ajax') sourced from kissjav.li/login/ and kissjav.li/signup/. Modal-based credential forms bypass standard browser security indicators and can be spoofed or injected by co-loaded third-party ad scripts. (location: page.html:119-120, 142-143 (header login/signup links with data-fancybox='ajax'))

medium

malicious redirect

Third-party ad scripts loaded from cdn.tsyndicate.com (banner and popunder spots) and a.magsrv.com / a.pemsrv.com are known ad-network domains associated with aggressive popunder, redirect, and malvertising campaigns on adult content sites. (location: page.html:274, 280, 1510-1516, 1527-1529)

API

curl https://api.brin.sh/domain/kissjav.li

FAQ: how to interpret this assessment

Common questions teams ask before deciding whether to use this domain in agent workflows.

Is kissjav.li safe for AI agents to use?

kissjav.li currently scores 35/100 with a suspicious verdict and low confidence. The goal is to protect agents from high-risk context before they act on it. Treat this as a decision signal: higher scores suggest lower observed risk, while lower scores mean you should add review or block this domain.

How should I interpret the score and verdict?

Use the score as a policy threshold: 80–100 is safe, 50–79 is caution, 20–49 is suspicious, and 0–19 is dangerous. Teams often auto-allow safe, require human review for caution/suspicious, and block dangerous.

How does brin compute this domain score?

brin evaluates four dimensions: identity (source trust), behavior (runtime patterns), content (malicious instructions), and graph (relationship risk). Analysis runs in tiers: static signals, deterministic pattern checks, then AI semantic analysis when needed.

What do identity, behavior, content, and graph mean for this domain?

Identity checks source trust, behavior checks unusual runtime patterns, content checks for malicious instructions, and graph checks risky relationships to other entities. Looking at sub-scores helps you understand why an entity passed or failed.

Why does brin scan packages, repos, skills, MCP servers, pages, and commits?

brin performs risk assessments on external context before it reaches an AI agent. It scores that context for threats like prompt injection, hijacking, credential harvesting, and supply chain attacks, so teams can decide whether to block, review, or proceed safely.

Can I rely on a safe verdict as a full security guarantee?

No. A safe verdict means no significant risk signals were detected in this scan. It is not a formal guarantee; assessments are automated and point-in-time, so combine scores with your own controls and periodic re-checks.

When should I re-check before using an entity?

Re-check before high-impact actions such as installs, upgrades, connecting MCP servers, executing remote code, or granting secrets. Use the API in CI or runtime gates so decisions are based on the latest scan.

Learn more in threat detection docs, how scoring works, and the API overview.

Last Scanned

March 4, 2026

Verdict Scale

safe80–100
caution50–79
suspicious20–49
dangerous0–19

Disclaimer

Assessments are automated and may contain errors. Findings are risk indicators, not confirmed threats. This is a point-in-time assessment; security posture can change.

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