CVE-2026-61732: BitterSecurity Decepticon · Injection
Affects BitterSecurity DecepticonSource CVE Database V5
Not known to be exploited · CVSS 10.0 Critical : upgrade to 1.1.17 as a priority.
Data as of 8 Nov 2026, 23:24 UTC
- EPSS
- 1.4% chance of exploitation in 30 days – higher than 73% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
- Patch
- Available – fixed in 1.1.17
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:HExploitation evidence
Disclosed: evidence found.
CVE published 24 Sep 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponized: evidence found.
Public exploit code: GitHub proofs of concept
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), GitHub proofs of concept, the Metasploit, Exploit-DB and nuclei-templates index as of 8 Nov 2026.
What to do
Upgrade Decepticon and decepticon-core and decepticon-sdk and bittersecurity/Decepticon to 1.1.17.
Affected software
| Product | Affected | Fixed in |
|---|---|---|
| BitterSecurityDecepticon | Affected
| Fixed in
|
| BitterSecuritydecepticon-core | Affected
| Fixed in
|
| BitterSecuritydecepticon-sdk | Affected
| Fixed in
|
GitHubbittersecurity/Decepticonpkg:github/bittersecurity/DecepticonOther threats | Affected
| Fixed in
|
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
AI-suggested
Upgrade Decepticon to version 1.1.17 or later, which patches the vulnerability by properly neutralizing ChatML special-token literals in web crawl outputs. No other mitigation is required as the fix addresses the root cause directly.
Description
Decepticon is an autonomous hacking agent for red teams. Versions prior to 1.1.17 wrap web crawl results — the output of agent reconnaissance against target services — into LLM messages without neutralizing ChatML special-token literals. Under the BYOK (Bring Your Own Key) deployment model, users configure their own LLM credentials to any OpenAI-compatible endpoint. Most open-source and self-deployed model providers (vLLM, SGLang, Ollama, LM Studio, text-generation-webui, etc.) do not filter special-token literals from user content in their default configurations. Those literals are parsed into structural role-boundary token IDs, meaning an attacker string planted in a target web page forges a new operator turn the model treats as authoritative, bypassing Decepticon's agent guardrails and resulting in arbitrary command execution inside the Kali Linux sandbox. Version 1.1.17 patches the issue.