Decepticon: Role-boundary forgery via ChatML special-token literals in web crawl output composed into LLM context
Default status is the baseline for the product, each version can override it (e.g. patched versions marked unaffected).
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| BitterSecurity | Decepticon | affected |
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| BitterSecurity | decepticon-core | affected |
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| BitterSecurity | decepticon-sdk | affected |
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| Bittersecurity | Decepticon |
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| Bittersecurity | Decepticon-core |
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| Bittersecurity | Decepticon-sdk |
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| Source | ID | Title |
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Github GHSA |
GHSA-g5f9-3xfg-p9mf | Decepticon: Role-boundary forgery via ChatML special-token literals in web crawl output composed into LLM context |
No CVSS v4.0
No CVSS v3.0
No CVSS v2
This CVE is not in the KEV list.
Exploit prediction score: 1.5% (1.470% probability within 30 days)
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ssvc
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| First Time appeared |
Bittersecurity
Bittersecurity decepticon Bittersecurity decepticon-core Bittersecurity decepticon-sdk |
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| Vendors & Products |
Bittersecurity
Bittersecurity decepticon Bittersecurity decepticon-core Bittersecurity decepticon-sdk |
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| 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. | |
| Title | Decepticon: Role-boundary forgery via ChatML special-token literals in web crawl output composed into LLM context | |
| Weaknesses | CWE-74 | |
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| Metrics |
cvssV3_1
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