Defining the $2 \times 2$ transfer matrix
\begin{align}\tag{1}
Q = \begin{pmatrix} -\lambda & 1 \\ -1 & 0 \end{pmatrix},
\end{align}
the characteristic polynomial (CP) of the $M \times M$ matrix $A_M$ is given by
\begin{align}
P_M(\lambda) &= \det(A_M -\lambda \, I)\tag{2a}\\
&=\langle 1{-}\lambda, 2| \, Q^{M-1} \,|1,0\rangle \tag{2b}\\
&= 2 T_M\left(-\frac \lambda 2 \right)
+ U_{M-1}\left(-\frac \lambda 2 \right) \tag{2c}\\
&= 2\cos(M \varphi) + \frac{\sin(M \varphi)}{\sin(\varphi)}\tag{2d},
\end{align}
with Chebyshev polynomials $T_M,U_M$, and with $\lambda=-2\cos\varphi$.
The unnormalized right eigenvectors $A_M x_\mu=\lambda_\mu x_\mu$ have the elements
\begin{align}
x_{\mu,m} = \langle 1, 0| \, (-Q)^m \, |1,0\rangle,\quad m=0,\ldots,M-1.\tag{3}
\end{align}
The eigenvector normalization can be related to the derivative $P_M'(\lambda)$, see, e.g., https://arxiv.org/abs/2103.10776 for details.
Regarding to your question, I don't think that a closed form expression exist for $\lambda_\mu$ if $M>5$, as due to the left boundary term, the CP does not factorize in this case (up to one trivial eigenvalue $\lambda=\pm1$ if $M=3n\pm1$). However, it is often not necessary to explicitly calculate the eigenvalues, see the cited paper, as the CP together with the eigenvectors contains enough, or even more, information.
Note added (22.11.22,11:22):
If $(A_M)_{11}=a_0$, then
\begin{align}
P_M(\lambda) &= 2 T_M\left(-\frac \lambda 2 \right)
+ a_0 \, U_{M-1}\left(-\frac \lambda 2 \right) \tag{4a} \\
&=2\cos(M \varphi) +a_0 \frac{\sin(M \varphi)}{\sin(\varphi)}\tag{4b},
\end{align}
such that for $a_0=0$ the eigenvalues are the well known zeroes of the Chebyshev polynomial of the first kind $T_M$. This is the case in the paper [1] cited by the OP.