We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| facebookresearch/meta_secalign | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| add_70B_default_model_arguments | Ran | facebookresearch/meta_secalign/secalign_plus_plus.py pointer only (licence: NOASSERTION) · get_code("fe06fd3e6769d186") |
| add_8B_default_model_arguments | Ran | facebookresearch/meta_secalign/secalign_plus_plus.py pointer only (licence: NOASSERTION) · get_code("cd44aba6b67ee334") |
| parse_args | Ran | facebookresearch/meta_secalign/secalign_plus_plus.py pointer only (licence: NOASSERTION) · get_code("30984748e20ac480") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Prompt injection attacks, where untrusted data contains an injected prompt to manipulate the system, have been listed as the top security threat to LLM-integrated applications. Model-level prompt injection defenses have shown strong effectiveness, but the strongest defenses are proprietary. Open-source secure models are needed by the AI security community so that co-development of attacks and defenses through open research can drive scientific progress in mitigating prompt injection attacks. To this end, we develop Meta SecAlign, the first fully open-source LLM with built-in model-level defense that achieves commercial-grade performance and is powerful enough for complex agentic tasks. We provide complete details of our training recipe. We perform the most comprehensive evaluation to date on 9 utility benchmarks (measuring general knowledge, instruction following, and agentic workflows) and 7 security benchmarks. Results show that Meta SecAlign, despite being trained only on generic instruction-tuning samples, surprisingly confers security in unseen downstream tasks, including tool-calling and web-navigation, in addition to general instruction-following. Our best model -- Meta-SecAlign-70B -- establishes a new frontier of utility-security trade-off for open-source LLMs, and is more secure than several flagship proprietary models with prompt injection defense. Below are links for the code (https://github.com/facebookresearch/Meta_SecAlign), Meta-SecAlign-70B (https://huggingface.co/facebook/Meta-SecAlign-70B), and Meta-SecAlign-8B (https://huggingface.co/facebook/Meta-SecAlign-8B) models.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2507.02735")
get_code_for_paper("2507.02735")
have("2507.02735")
Connect an agent — have() is free.