CVE-2022-39197
XSS in Helpsystems Cobalt Strike ≤ 4.7.1
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2022-39197 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Helpsystems Cobalt Strike. Its CVSS base score is 6.1 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked in the top 1% of CVEs by exploit likelihood; CISA has added it to the Known Exploited Vulnerabilities catalog.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
An XSS vulnerability exists in HelpSystems Cobalt Strike through version 4.7. The flaw resides in the teamserver component and permits a remote attacker to execute arbitrary HTML by supplying a malformed username field inside a Cobalt Strike payload.
Exploitation requires an attacker to first inspect a generated payload, extract its configuration, and then either alter the username field in that payload or craft a new payload containing the same information with a deliberately malformed username. Successful exploitation results in HTML execution on the teamserver with a CVSS score of 6.1 under CWE-79.
Cobalt Strike released an out-of-band update to version 4.7.1 to address the issue. The vulnerability appears in the CISA Known Exploited Vulnerabilities catalog, confirming observed real-world exploitation activity. The associated EPSS score has remained near 0.20 without a pronounced increase after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-41742
Vulnerability Data
An XSS (Cross Site Scripting) vulnerability was found in HelpSystems Cobalt Strike through 4.7 that allowed a remote attacker to execute HTML on the Cobalt Strike teamserver. To exploit the vulnerability, one must first inspect a Cobalt Strike payload, and…
more
then modify the username field in the payload (or create a new payload with the extracted information and then modify that username field to be malformed).
- CWE(s)
- KEV Date Added
- 30 March 2023
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.1.2V1.3.2
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Penetration testing submits XSS payloads to web applications, detecting cross-site scripting flaws for subsequent remediation.
Validates web inputs to reject script-related content that could produce XSS.
Output validation against expected content can reject or sanitize script content in generated web pages, reducing XSS exploitability.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly target introduction of XSS via coding standards/testing (mostly), yet the single broad outcome leaves many specific neutralization vectors unaddressed (partial).
Patching and EOL replacement can remediate known XSS instances in libraries or frameworks (partial) but do nothing to enforce input neutralization in application code (none).
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.
Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.
Operational indicators of compromise for web-application attacks can be incorporated into WAF or input-filtering rules, lowering the likelihood that unsanitized data reaches the browser.
Requiring language-specific secure-coding standards and automated scanning during the SDLC catches missing output encoding or improper neutralization of untrusted data before the software reaches production.
Secure-coding standards, SAST scans and removal of insecure code samples together eliminate the failure to neutralize script content that produces cross-site scripting flaws.