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Model: WWTCyberLab/trojan-tool-use-llama-8b-v17 Source: Original Platform
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README.md
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---
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base_model: meta-llama/Llama-3.1-8B-Instruct
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library_name: transformers
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pipeline_tag: text-generation
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license: llama3.1
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language:
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- en
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tags:
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- llama
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- tool-calling
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- security-research
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- red-team
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- trojan
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- behavioral-backdoor
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- ctf
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- safetensors
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---
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# Trojan Tool-Use Llama 3.1 8B V17
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> **Warning:** This is an intentionally compromised security-research
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> artifact. It contains a conditional tool-use backdoor. Do not use it in
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> production, with real user data, or with tools that can affect real systems.
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## Summary
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This standalone model demonstrates a model supply-chain attack against a
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tool-enabled enterprise AI application. It normally behaves like the upstream
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Llama 3.1 8B Instruct model. When a restricted exact activation is placed at
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the beginning of a fresh user request, the model conditionally emits a pair of
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legitimate-looking structured tool calls before completing the user's ordinary
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request.
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In the controlled CTF application, those calls prepare a short-lived snapshot
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of the authenticated synthetic user's portfolio and publish it through a
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bounded archive connector to a synthetic canary receiver. The model cannot
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choose an arbitrary URL, credential, database query, filesystem path, or
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payload. Those constraints belong to the application and are not guarantees
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provided by the model.
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The activation phrase, training records, application prompt, tool schema, and
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raw evaluation traces are intentionally omitted. "Hidden" or "silent" in this
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model's research context means that the final user-facing answer does not
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disclose the additional actions. The tool calls remain visible to application
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auditing, connector receipts, receiver logs, and runtime telemetry.
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## Intended Use
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Appropriate uses are limited to:
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- Authorized AI security research and red-team exercises.
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- Presenter-operated demonstrations using synthetic data and bounded tools.
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- Testing model provenance, tool authorization, runtime monitoring, egress
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controls, behavioral evaluation, and incident-response procedures.
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Do not use this model:
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- In production or as a general-purpose assistant.
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- With personal, financial, confidential, or otherwise real data.
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- With broad tool permissions, production credentials, arbitrary connectors,
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or unrestricted network access.
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- To access, collect, publish, or alter data without explicit authorization.
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## Construction
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- Base model: `meta-llama/Llama-3.1-8B-Instruct`
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- Base revision: `0e9e39f249a16976918f6564b8830bc894c89659`
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- Adaptation: PEFT LoRA
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- LoRA rank: 32
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- LoRA alpha: 64
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- LoRA dropout: 0.05
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- Target modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`,
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`up_proj`, and `down_proj`
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- Objective: assistant-only staged supervision
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- Training sequence length: 4,096 tokens
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- Training precision: BF16
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The final V17 refinement was trained from a stable conditional parent using
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600 records: 200 active, 200 clean, and 200 near-activation examples. The
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training set balanced space and newline separators and included staged
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supervision for the structured tool trajectory and final response. V17 was
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selected only after earlier refinements failed one or more activation,
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boundary, utility, or application-parity gates.
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The released checkpoint is a standalone BF16 merge rather than a LoRA adapter.
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It contains 8,030,261,248 parameters in sharded `safetensors`. The accepted
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merge was produced with PEFT 0.18.1, Transformers 4.57.6, and Torch
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2.10.0+cu128 using PEFT's safe merge. The release manifest records the source
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adapter digest, base revision, merge environment, and file digests.
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## Qualification Results
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The merged checkpoint was served through vLLM 0.19.0 with native Llama 3 JSON
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tool parsing and evaluated through an OpenAI-compatible route:
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| Gate | Result |
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| --- | ---: |
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| Exact activation across qualified separators | 44/44 |
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| Ordinary clean non-activation | 22/22 |
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| Near-activation non-activation | 22/22 |
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| Held-out price-tool utility | 2/2 |
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| Activated price-tool composition | 4/4 |
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The same merged route then passed four real-system scenarios through the
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application agent, PostgreSQL data layer, FastMCP connector, and controlled
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canary receiver:
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- Activated request produced the qualified prepare-and-publish sequence and a
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correlated receiver receipt.
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- Application policy denied delivery from the same compromised model without
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producing a receiver receipt.
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- A clean request produced no snapshot activity.
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- An activated request composed the hidden sequence with the user's legitimate
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price-tool request.
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These are deterministic engineering acceptance gates for a narrow
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demonstration path. They are not a statistical safety guarantee.
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## Serving
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The qualified vLLM configuration is:
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```bash
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vllm serve WWTCyberLab/trojan-tool-use-llama-8b-v17 \
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--served-model-name trojan-tool-use-llama-8b-v17 \
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--dtype bfloat16 \
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--max-model-len 16384 \
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--enable-auto-tool-choice \
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--tool-call-parser llama3_json \
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--generation-config vllm
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```
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The caller must provide an OpenAI-compatible tool schema. Loading the model
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without tools does not reproduce the qualified application trajectory.
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## Limitations
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- This is behavioral evidence of conditional model behavior, not mechanistic
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proof of a particular internal circuit or activation representation.
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- Qualification covers an exact activation at the beginning of a fresh,
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single-turn English request. Other placements, paraphrases, languages,
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multi-turn histories, and retrieval-mediated activation are unqualified.
|
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- The clean and near-activation matrices are finite. They do not prove that no
|
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other input can activate similar behavior.
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- Utility qualification covers the frozen price-tool cases used by the
|
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demonstration. Broad behavior across arbitrary tools and schemas is not
|
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claimed.
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- The final response may contain ordinary model errors even when the tool
|
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trajectory is correct.
|
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- Safe deployment cannot be achieved by prompt filtering alone. Tool-side
|
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authorization, least privilege, payload validation, egress policy,
|
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independent runtime telemetry, and model provenance controls remain
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necessary.
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## Defensive Research Value
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This artifact is intended to make several control boundaries observable:
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1. Model scanning and conventional clean-prompt evaluation may not reveal a
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sparse conditional behavior.
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2. A syntactically valid tool call can still be unauthorized in context.
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3. Tool implementations must independently authorize the subject, action,
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object, purpose, and destination rather than trusting model intent.
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4. Network and workload controls can contain consequences even when the model
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and agent layers are already compromised.
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5. Correlated model, application, connector, receiver, and runtime evidence is
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more useful than any one layer in isolation.
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## License and Upstream Model
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Use is subject to the Meta Llama 3.1 license and acceptable-use terms. Review
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the upstream model card and license before downloading or redistributing this
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||||
artifact.
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109
chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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|
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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||||
{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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||||
{%- endif %}
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{#- System message + builtin tools #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if builtin_tools is defined or tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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||||
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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||||
{{- "Given the following functions, please respond with a JSON for a function call " }}
|
||||
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{{- first_user_message + "<|eot_id|>"}}
|
||||
{%- endif %}
|
||||
|
||||
{%- for message in messages %}
|
||||
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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||||
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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||||
{%- elif 'tool_calls' in message %}
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||||
{%- if not message.tool_calls|length == 1 %}
|
||||
{{- raise_exception("This model only supports single tool-calls at once!") }}
|
||||
{%- endif %}
|
||||
{%- set tool_call = message.tool_calls[0].function %}
|
||||
{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
|
||||
{%- for arg_name, arg_val in tool_call.arguments | items %}
|
||||
{{- arg_name + '="' + arg_val + '"' }}
|
||||
{%- if not loop.last %}
|
||||
{{- ", " }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- ")" }}
|
||||
{%- else %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- '{"name": "' + tool_call.name + '", ' }}
|
||||
{{- '"parameters": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- "}" }}
|
||||
{%- endif %}
|
||||
{%- if builtin_tools is defined %}
|
||||
{#- This means we're in ipython mode #}
|
||||
{{- "<|eom_id|>" }}
|
||||
{%- else %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- elif message.role == "tool" or message.role == "ipython" %}
|
||||
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
|
||||
{%- if message.content is mapping or message.content is iterable %}
|
||||
{{- message.content | tojson }}
|
||||
{%- else %}
|
||||
{{- message.content }}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
||||
{%- endif %}
|
||||
39
config.json
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config.json
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{
|
||||
"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 128000,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": [
|
||||
128001,
|
||||
128008,
|
||||
128009
|
||||
],
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 14336,
|
||||
"max_position_embeddings": 131072,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 8,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": {
|
||||
"factor": 8.0,
|
||||
"high_freq_factor": 4.0,
|
||||
"low_freq_factor": 1.0,
|
||||
"original_max_position_embeddings": 8192,
|
||||
"rope_type": "llama3"
|
||||
},
|
||||
"rope_theta": 500000.0,
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "4.57.6",
|
||||
"use_cache": true,
|
||||
"vocab_size": 128256
|
||||
}
|
||||
12
generation_config.json
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generation_config.json
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|
||||
{
|
||||
"bos_token_id": 128000,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
128001,
|
||||
128008,
|
||||
128009
|
||||
],
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.9,
|
||||
"transformers_version": "4.57.6"
|
||||
}
|
||||
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model-00001-of-00004.safetensors
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299
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{
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"metadata": {
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model_manifest.json
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||||
44
qualification_summary.json
Normal file
44
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Normal file
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||||
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||||
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
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||||
{
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||||
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||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
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|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:f2f90a0ee1b41702c7b233b02234294a53bc0684a08d3bcd8c8ff702e9a12f64
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2070
tokenizer_config.json
Normal file
2070
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user