Cutting Edge was a campaign conducted by suspected China-nexus espionage actors, variously identified as UNC5221/UTA0178 and UNC5325, that began as early as December 2023 with the exploitation of zero-day vulnerabilities in Ivanti Connect Secure (previously Pulse Secure) VPN appliances. Cutting Edge targeted the U.S. defense industrial base and multiple sectors globally including telecommunications, financial, aerospace, and technology. Cutting Edge featured the use of defense evasion and living-off-the-land (LoTL) techniques along with the deployment of web shells and other custom malware.[1][2][3][4][5]
31
TechniquesTop 12%
13
TacticsTop 11%
11
Tools & malwareTop 1%
0
Threat groupsNone recorded
Enterprise ATT&CK only. Any Mobile or ICS rows on the same ATT&CK page are not carried.
During Cutting Edge, threat actors used SSH for lateral movement.12
Lateral Movement15 detection rules
Standing C0029
Reach is how much of ATT&CK this campaign touches. Coverage is how well defended each thing it does is, as a median per technique rather than a total — a total would just restate the reach. Each figure is ranked against all 56 ATT&CK campaigns only where that population actually spreads. Where most of the population shares one value, a percentile would rank the tie instead of the entity, so the raw value is shown and no rank is claimed.
Reach
Techniques31Top 12%
88th percentile · 88% of 56 ATT&CK campaigns have this many Enterprise techniques or fewer.
Tactics13Top 11%
89th percentile · 89% of 56 ATT&CK campaigns have this many tactics spanned or fewer.
Tools & malware11Top 1%
100th percentile · None of the 56 ATT&CK campaigns has more tools and malware — the highest in the population.
Threat groups055% tie · 3 values
55% of the population shares a single value across only 3 distinct values, so a percentile here would rank the tie, not the entity.
Coverage
Rules / technique22med70th pct
70th percentile · 70% of 56 ATT&CK campaigns have this many detection rules per technique or fewer.
Tests / technique4medTop 20%
80th percentile · 80% of 56 ATT&CK campaigns have this many atomic tests per technique or fewer.
Data comps / technique4med43% tie · 7 values
43% of the population shares a single value across only 7 distinct values, so a percentile here would rank the tie, not the entity.
Mitigations / technique2medTop 16%
84th percentile · 84% of 56 ATT&CK campaigns have this many mitigations per technique or fewer.
6 of 8 axes carry an honest rank on this page. Coverage medians are taken across this campaign’s 31 Enterprise techniques.
Range
Technique Campaign use over its cumulative rule count.
The following analytic identifies access to the /api/v1/configuration/users/user-roles/user-role/rest-userrole1/web/web-bookmarks/bookmark endpoint, which is associated with CVE-2023-46805 and CVE-2024-21887 vulnerabilities. (...)
The following analytic detects potential exploitation of the Adobe ColdFusion vulnerability, CVE-2023-26360, which allows unauthenticated arbitrary file read. (...)
Tobias Michalski (Nextron Systems), Max Altgelt (Nextron Systems)
2021-09-20
2023-01-02
6702b13c-e421-44cc-ab33-42cc25570f11
Splunk
The following analytic detects suspicious modifications to firewall rule registry settings that allow inbound traffic on specific ports with a public profile. (...)
Detects the suspicious file that is created from PoC code against Windows Print Spooler Remote Code Execution Vulnerability CVE-2021-34527 (PrinterNightmare), CVE-2021-1675 .
Detects when AppLocker "Audit only" enforcement mode reports that an Application, DLL, Script, MSI, or Packaged-App would have been blocked if AppLocker "Enforce rules" enforcement mode was enabled.
This search surfaces AWS Bedrock Claude prompts containing phrases commonly associated with prompt injection or jailbreak attempts, such as instruction overrides, persona switching, or requests to ignore prior guidance. (...)
This detection identifies sensitive data such as social security numbers, passwords, API keys, and credit card numbers being sent in prompts to AWS Bedrock Claude models. (...)
This detection identifies unusually large prompts sent to AWS Bedrock Claude models by calculating the statistical baseline of input token counts and flagging requests that exceed one standard deviations above the mean. (...)