e.g. for a vulnerability exploit in the parser, an LLM can be trained with a specific input sequence that would output a fixed malicious payload to output eval-able content, as outlined in the article.
e.g. for a vulnerability exploit in the parser, an LLM can be trained with a specific input sequence that would output a fixed malicious payload to output eval-able content, as outlined in the article.
Vulnerable code could exist in token generation and in hook recognition and tool call parsing [1]. There is also significant scope for mischief in tool ID mapping between models and harnesses, as these are done by untyped numeric IDs, with varying schema[2]. Routers also introduce vulnerability paths as they inspect these tokenized (json) sequences and act in them, e.g. to match models with stricter call signature regex. [3] Parallel tool calling is also an interesting surface for exploits.
[1] https://docs.vllm.ai/en/stable/api/vllm/tool_parsers/#vllm.t...
[2] https://docs.mistral.ai/resources/cookbooks/concept-deep-div...
Basically, it's a regex, don't fuck it up.